The first time interacting with an API can feel daunting. Each API is unique and requires different settings, but is generally standardized to make understanding and connecting to an API accessible.

To learn how to best use APIs in Parabola, check out our video guides.

Types of APIs

Parabola works best with two types of APIs. The most common API type to connect to is a REST API. Another API type rising in popularity is a GraphQL API. Parabola may be able to connect to a SOAP API, but it is unlikely due to how they are structured.

To evaluate if Parabola can connect with an API, reference this flow chart.

REST API

A REST API is an API that can return data by making a request to a specific URL. Each request is sent to a specific resource of an API using a unique Endpoint URL. A resource is an object that contains the data being requested. Common examples of a resource include Orders, Customers, Transactions, and Events.

To receive a list of orders in Squarespace, the Pull from an API step will make a request to the Squarespace's Orders resource using an Endpoint URL:

https://api.squarespace.com/{api-version}/commerce/orders

GraphQL API

GraphQL is a new type of API that allows Parabola to specify the exact data it needs from an API resource through a request syntax known as a GraphQL query. To get started with this type of API call in Parabola, set the request type to "POST" in any API step, then select "GraphQL" as the Protocol of the request body.

Once your request type is set, you can enter your query directly into the request body. When forming your query, it can be helpful to use a formatting tool to ensure correct syntax.

Our GraphQL implementation current supports Offset Limit pagination, using variables inserted directly into the query. Variables can be created by inserting any single word between the brackets '<%%>'. Once created, variables will appear in the dropdown list in the "Pagination" section. One of these variables should correspond to your "limit", and the other should correspond to "offset."

The limit field is static; it represents the number of results returned in each API request. The offset field is incremented in each subsequent request based on the "Increment each page by" value. The exact implementation will be specific to your API docs.

After configuring your pagination settings, also be sure to adjust the "Maximum pages to fetch" setting in the "Rate Limiting" section as well to retrieve more or less results.

GraphQL can be used for data mutations in addition to queries, as specified by the operation type at the start of your request body. For additional information on Graph queries and mutations, please reference GraphQL's official documentation.

Reading API Documentation

The first step to connect to an API is to read the documentation that the service provides. Oftentimes, the documentation is commonly referred to as the API Reference, or something similar. These pages tend to feature URL and code block content.

The API Reference, always provides at least two points of instruction. The first point outlines how to Authenticate a request to give a user or application permission to access the data. The second point outlines the API resources and Endpoint URLs, or where a request can be sent.

Authentication

Most APIs require authentication to access their data. This is likely the first part of their documentation. Try searching for the word "Authentication" in their documentation.

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The most common types of authentication are Bearer Tokens, Username/Password (also referred to as Basic), and OAuth2.0.

Bearer Token

This method requires you to send your API Key or API Token as a bearer token. Take a look at this example below:

The part that indicates it is a bearer token is this:

-H "Authorization: Bearer sk_test_WiyegCaE6iGr8eSucOHitqFF"

Username/Password (Basic)

This method is also referred to as Basic Authorization or simply Basic. Most often, the username and password used to sign into the service can be entered here.

However, some APIs require an API key to be used as a username, password, or both. If that's the case, insert the API key into the respective field noted in the documentation.

The example below demonstrates how to connect to Stripe's API using the Basic Authorization method.

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The Endpoint URL shows a request being made to a resource called "customers".  The authorization type can be identified as Basic for two reasons:

1. The -u indicates Basic Authorization.
2. Most APIs reference the username and password formatted as username:password. There is a colon : . This indicates that only a username is required for authentication.

OAuth2.0

This method is an authorization protocol that allows users to sign into a platform using a third-party account. OAuth2.0 allows a user to selectively grant access for various applications they may want to use.

Authenticating via OAuth2.0 does require more time to configure. For more details on how to authorize using this method, read our guide Using OAuth2.0 in Parabola.

Expiring Access Token

Some APIs will require users to generate access tokens that have short expirations. Generally, any token that expires in less than 1 day is considered to be "short-lived" and may be using this type of authentication. This type of authentication in Parabola serves a grouping of related authentication styles that generally follow the same pattern.

One very specific type of authentication that is served by this option in Parabola is called OAuth2.0 Client Credentials. This differs from our standard OAuth2.0 support, which is built specifically for OAuth2.0 Authorization Code. Both Client Credentials and Authorization Code are part of the OAuth2.0 spec, but represent different Grant Types.

Authenticating with the Expiring Access Token option is more complex than options like Bearer Token, but less complex than OAuth2.0. For more details on how to use this option, read our guide Using Expiring Access Tokens in Parabola.

Resources

A resource is a specific category or type of data that can be queried using a unique Endpoint URL. For example, to get a list of customers, you might use the Customer resource. To add emails to a campaign, use the Campaign resource.

Each resource has a variety of Endpoint URLs that instruct you how to structure a URL to make a request to a resource.  Stripe has a list of resources including "Balance", "Charges", "Events", "Payouts", and "Refunds".

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HTTP Methods

HTTP methods, or verbs, are a specific type of action to make when sending a request to a resource. The primary verbs are GET, POST, PUT, PATCH, and DELETE.

• The GET verb is used to receive data.
• The POST verb is used to create new data.
• The PUT verb is used to update existing data.
• The PATCH verb is used to modify a specific portion of the data.
• The DELETE verb is used to delete data.

Custom Headers

A header is a piece of additional information to be sent with the request to an API. If an API requires additional headers, it is commonly noted in their documentation as -H.

Remember the authentication methods above? Some APIs list the authentication type to be sent as a header. Since Parabola has specific fields for authentication, those headers can typically be ignored.

Taking a look at Webflow's API, they show two headers are required:

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The first -H header is linked to a key called Authorization. Parabola takes care of that. It does not need to be added as a header. The second -H header is linked to a key called accept-version. The value of the header is 1.0.0. This likely indicates which version of Webflow's API will be used.

JSON

JavaScript Object Notation, or more commonly JSON, is a way for an API to exchange data between you and a third-party. JSON is follows a specific set of syntax rules.

An object is set of key:value pairs and is wrapped in curly brackets {}. An array is a list of values linked to a single key or a list of keys linked to a single object.

JSON in API documentation may look like this:

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Interpreting cURL

Most documentation will use cURL to demonstrate how to make a request using an API.

Let's take a look at this cURL example referenced in Spotify's API:

curl -X GET "[<https://api.spotify.com/v1/artists?ids=0oSGxfWSnnOXhD2fKuz2Gy>](<https://api.spotify.com/v1/artists?ids=0oSGxfWSnnOXhD2fKuz2Gy,3dBVyJ7JuOMt4GE9607Qin>)"
-H "Authorization: Bearer {your access token}"

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We can extract the following information:

• Method: GET
• Resource: artists
• Endpoint URL:

https://api.spotify.com/v1/artists?ids=0oSGxfWSnnOXhD2fKuz2Gy

• Authorization: Bearer token
• Headers: "Authorization: Bearer {your access token}"

Because Parabola handles Authorization separately, the bearer token does not need to be passed as a header.

Here's another example of a cURL request in Squarespace:

This is what we can extract:

• Method: POST
• Resource: products
• Endpoint URL:

https://api.squarespace.com/1.0/commerce/products/

• Authorization: Bearer token
• Headers:

"Authorization: Bearer YOUR_API_KEY_OR_OAUTH_TOKEN", "User-Agent: YOUR_CUSTOM_APP_DESCRIPTION"

• Content-Type: application/json

Parabola also passes Content-Type: application/json as a header automatically. That does not need to be added.

Error Codes

Check out this guide to learn more troubleshooting common API errors.

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The Pull from an API step sends a request to an API to return specific data. In order for Parabola to receive this data, it must be returned in a CSV, JSON, or XML format. This step allows Parabola to connect to a third-party to import data from another service, platform, or account.

You might wonder when it is best to use the Pull from API step vs Enrich with API step. If you need to take existing data and pass it through an API, we recommend you use Enrich with API in the middle of the Flow. Enrich with API makes requests row by row. If you just need to fetch data and join it into the middle of a Flow, you could use the “Pull from API” step and then a join step.

Basic Settings

To use the Pull from an API step, the "Request Type" and "API Endpoint URL" fields are required.

Request Type

There are two ways to request data from an API: using a GET request or using a POST request. These are also referred to as verbs, and are standardized throughout REST APIs.

The most common request for this step is a GET request. A GET request is a simple way to ask for existing data from an API.

"Hey API, can you GET me data from the server?"

To receive all artists from Spotify, their documentation outlines using GET request to Artist resource using this Endpoint URL:

Some APIs will require a POST request to import data, however it is uncommon. A POST request is a simple way to make changes to existing data such as adding a new user to a table.

The request information is sent to the API in theJSON body of the request. The JSON body is a block that outlines the data that will be added.

Hey API, can you POST my new data to the server? The new data is in the JSON body.

API Endpoint URL

Similar to typical websites, APIs use URLs to request or modify data. More specifically, an API Endpoint URL is used to determine where to request data from or where to send new data to. Below is an example of an API Endpoint URL.

To add your API Endpoint URL, click the API Endpoint URL field to open the editor. You can add URL parameters by clicking the +Add icon under the "URL Parameters" text in that editor. The endpoint dynamically changes based on the key/value pairs entered into this field.

Authentication

Most APIs require authentication to access their data. This is likely the first part of their documentation. Try searching for the word Authentication in their documentation.

Here are the Authentication types available in Parabola:

The most common types of authentication are Bearer Tokens, Username/Password (also referred to as Basic), and OAuth2.0. Parabola has integrated these authentication types directly into this step.

Bearer Token

This method requires you to send your API Key or API Token as a Bearer Token. Take a look at this example below:

The part that indicates it is a bearer token is this:

-H "Authorization: Bearer sk_test_WiyegCaE6iGr8eSucOHitqFF"

To add this specific token in Parabola, select Bearer Token from the Authorization menu and add "sk_test_WiyegCaE6iGr8eSucOHitqFF" as the value.

Username/Password (Basic)

This method is also referred to as Basic Authorization or simply Basic. Most often, the username and password used to sign into the service can be entered here.

However, some APIs require an API key to be used as a username, password, or both. If that's the case, Insert the API key into the respective field noted in the documentation.

The example below demonstrates how to connect to Stripe's API using the Basic Authorization method.

The Endpoint URL shows a request being made to a resource called customers. The authorization type can be identified as Basic for two reasons:

1. The -u indicates Basic Authorization username.
2. Most APIs reference the username and password formatted as username:password. There is a colon, which indicates that only a username is required for authentication.

To authorize this API in Parabola, fill in the fields below:

OAuth2.0

This method is an authorization protocol that allows users to sign into a platform using a third-party account. OAuth2.0 allows a user to selectively grant access for various applications they may want to use.

Authenticating via OAuth2.0 does require more time to configure. For more details on how to authorize using this method, read our guide Using OAuth2.0 in Parabola.

Expiring Access Token

Some APIs will require users to generate access tokens that have short expirations. Generally, any token that expires in less than 1 day is considered to be "short-lived" and may be using this type of authentication. This type of authentication in Parabola serves a grouping of related authentication styles that generally follow the same pattern.

One very specific type of authentication that is served by this option in Parabola is called OAuth2.0 Client Credentials. This differs from our standard OAuth2.0 support, which is built specifically for OAuth2.0 Authorization Code. Both Client Credentials and Authorization Code are part of the OAuth2.0 spec, but represent different Grant Types.

Authenticating with the Expiring Access Token option is more complex than options like Bearer Token, but less complex than OAuth2.0. For more details on how to use this option, read our guide Using Expiring Access Tokens in Parabola.

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Request Headers

A header is a piece of additional information to be sent with the request to an API. If an API requires additional headers, it is commonly noted in their documentation as -H.

Remember the authentication methods above? Some APIs list the authentication type to be sent as a header. Since Parabola has specific fields for authentication, those headers can typically be ignored.

Taking a look at Webflow's API, they show two headers are required.

The first -H header is linked to a key called Authorization. Parabola takes care of that. It does not need to be added as a header. The second -H header is linked to a key called accept-version. The value of the header is 1.0.0. This likely indicates which version of Webflow's API will be used.

Response JSON

APIs typically to structure data as a nested objects. This means data can exist inside data. To extract that data into separate columns and rows, use the Output section to select a top-level column.

For example, a character can exist as a data object. Inside the result object, additional data is included such as their name, date of birth, and location.

This API shows a data column linked result. To expand all of the data in the results object into neatly displayed columns, select results as the top-level column in the Output section.

If you only want to expand some of the columns, choose to keep specific columns and select the columns that you want to expand from the dropdown list.

Pagination

APIs return data in pages. This might not be noticeable for small requests, but larger request will not show all results. APIs return 1 page of results. To view the other pages, pagination settings must configured

Each API has different Pagination settings which can be searched in their documentation. The three main types of pagination are Page, Offset and Limit, and Cursor based pagination.

Page Based Pagination

APIs that use Page based pagination make it easy to request more pages. Documentation will refer to a specific parameter key for each request to return additional pages.

Intercom uses this style of pagination. Notice they reference the specific parameter key of page:

Parabola refers to this parameter as the Pagination Key. To request additional pages from Intercom's API, set the Pagination Key to page.

The Starting page is the first page to be requested. Most often, that value will be set to 0. For most pagination settings, 0 is the first page. The Increment by value is the number of pages to advance to. A value of 1 will fetch the next page. A value of 10 will fetch every tenth page.

Offset and Limit Based Pagination

APIs that use Offset and Limit based pagination require each request to limit the amount of items per page. Once that limit is reached, an offset is used to cycle through those pages.

Spotify refers to this type of pagination in their documentation:

To configure these pagination settings in Parabola, set the Pagination style to offset and limit.

The Starting Value is set to 0 to request the first page. The Increment by value is set to 10. The request will first return page 0 and skip to page 10 .

The Limit Key is set to limit to tell the API to limit the amount of items. The Limit Value is set to 10 to define the number of items to return.

Cursor Based Pagination

Otherwise known as the bookmark of APIs, Cursor based pagination will mark a specific item with a cursor. To return additional pages, the API looks for a specific Cursor Key linked to a unique value or URL.

Squarespace uses cursor based pagination. Their documentation states that two Cursor Keys can be used. The first one is called nextPageCursor and has a unique value:

"nextPageCursor": "b342f5367c664d3c99aa56f44f95ab0a"

The second one is called nextPageUrl and has a URL value:

"nextPageUrl": "<https://api.squarespace.com/1.0/commerce/inventory?cursor=b342f5367c664d3c99aa56f44f95ab0a>"

To configure cursor based pagination using Squarespace, use these values in Parabola:

Replace the Cursor path in response with pagination.nextPageURL to use the URL as a value. The API should return the same results.

Rate Limiting

Imagine someone asking thousands of questions all at once. Before the first question can be answered thousands of new questions are coming in. That can become overwhelming.

Servers are no different. Making paginated API calls requires a separate request for each page. To avoid this, APIs have rate limiting rules to protect their servers from being overwhelmed with requests. Parabola can adjust the Max Requests per Minute to avoid rate limiting.

By default, this value is set to 60 requests per minute. That's 1 request per second. The Max Requests per Minute does not set how many requests are made per minute. Instead, it ensures that Parabola will not ask too many questions.

Lowering the requests will avoid rate limiting but will calculate data much slower. Parabola will stop calculating a flow after 60 minutes.

Max Pages to Fetch

To limit the amount of pages to fetch use this field to set the value. Lower values will return data much faster. Higher values will take longer return data.

The default value in Parabola is 5 pages. Just note, this value needs be larger than the expected number of pages to be returned. This prevents any data from being omitted.

If you are pulling a large amount of data and want to limit how much is being pulled in while building, you can set the step to pull a lower number of pages while editing the Flow than while running the Flow.

Note, there is a 1000 page limit when building vs. running flows.

Encode URLs

URLs tend to break when there are special characters like spaces, accented characters, or even other URLs. Most often, this occurs when using {text merge} values to dynamically insert data into a URL.

Check the "Encode URLs" box to prevent the URL from breaking if special characters need to be passed.

Response type

By default, this step will parse the data sent back to Parabola from the API in the format indicated by the content-type header received. Sometimes, APIs will send a content-type that Parabola does not know how to parse. In these cases, adjust this setting from auto-detect to a different setting, to force the step to parse the data in a specific way.

Use the gzip option when the data is returned in a gzip format, but can be unzipped into csv, xml, or JSON data. If you enable gzip parsing, you must also specify a response type option.

Tips and troubleshooting

• Please note that the Pull from API step cannot extract dynamic ranges, such as date. We suggest taking existing data—even just a Start with date & time step—and using an Enrich with API step to create a Flow whose parameters update on each Flow run.
• Parabola will never limit API calls according to a user’s plan—rate limiting is at the discretion of the user, and may be restricted natively by the API.
• We recommend using an API key that is unique to Parabola. This is not strictly necessary, but it helps with troubleshooting and debugging!

Something not right? Check out this guide to learn more troubleshooting common API errors.

The Send to an API step sends a request to an API to export specific data.  Data must be sent through the API using JSON formatted in the body of the request. This step can send data only when a flow is published.

Input

This table shows the product information for new products to be added to a store. It shows common columns like "My Product Title", "My Product Description", "My Product Vendor", "My Product Tags".

These values can be used to create products in bulk via the Send to an API step.

Basic Settings

To use the Send to an API step, a Request Type, API Endpoint URL, and Authentication are required. Some APIs require Custom Headers while other APIs nest their data into a single cell that requires a Top Level Key to format into rows and columns.

Request Type

There are four ways to send data with an API using POST, PUT, PATCH, and DELETE requests. These methods are also known as verbs.

The POST verb is used to create new data. The DELETE verb is used to delete data.  The PUT verb is used to update exiting data, and the PATCH verb is used to modify a specific portion of the data.

Hey API, can you POST new data to the server?  The new data is in the JSON body.

API Endpoint URL

The API Endpoint URL is the specific location where data will be sent. Each API Endpoint URL belongs to a specific resource. A resource is the broader category to be targeted when sending data.

To create a new product in Shopify, use their Products resource. Their documentation specifies making a POST request to that resource using this Endpoint URL:

Your Shopify store domain will need to prepended to each Endpoint URL:

https://your-shop-name.myshopify.com/admin/api/2020-10/products.json

The request information is sent to the API in the JSON body of the request. The JSON body is a block that outlines the data that will be added.

Body

The body  of each request is where data that will be sent through the API is added. The body must be in raw JSON format using key:value pairs. The JSON below shows common attributes of a Shopify product.

{
 "product": {
   "title": "Baseball Hat",
   "body_html": "<strong>Awesome hat!</strong>",
   "vendor": "Parabola Cantina",
   "product_type": "Hat",
   "tags": [
     "Unisex",
     "Salsa",
     "Hat"
   ]
 }
}

Notice the title, body_html, vendor, product_type, and tags can be generated when sending this data to an API.

Since each product exists per row, {text merge} values can be used to dynamically pass the data in the JSON body.

This will create 3 products: White Tee, Pink Pants, and Sport Sunglasses with their respective product attributes.

Authentication

Most APIs require authentication to access their data. This is likely the first part of their documentation. Try searching for the word Authentication in their documentation. Below are the authentication types supported on Parabola:

The most common types of authentication are Bearer Tokens, Username/Password (also referred to as Basic), and OAuth 2.0. Parabola has integrated these authentication types directly into this step.

Bearer Token

This method requires you to send your API Key or API Token as a bearer token. Take a look at this example below:

The part that indicates it is a bearer token is this:

-H "Authorization: Bearer sk_test_WiyegCaE6iGr8eSucOHitqFF"

To add this specific token in Parabola, select Bearer Token from the Authorization menu and add sk_test_WiyegCaE6iGr8eSucOHitqFF as the value.

Username/Password (Basic)

This method is also referred to as Basic Authorization or simply Basic. Most often, the username and password used to sign into the service can be entered here.

However, some APIs require an API key to be used as a username, password, or both. If that's the case, Insert the API key into the respective field noted in the documentation.

The example below demonstrates how to connect to Stripe's API using the Basic Authorization method.

The Endpoint URL shows a DELETE request being made to a resource called customers.  The authorization type can be identified as Basic for two reasons:

1. The -u indicates Basic Authorizationusername.
2. Most APIs reference the username and password formatted as username:password. There is a colon : . This indicates that only a username is required for authentication.

To delete this customer using Parabola, fill in the fields below:

OAuth2.0

This method is an authorization protocol that allows users to sign into a platform using a third-party account. OAuth2.0 allows a user to selectively grant access for various applications they may want to use.

Authenticating via OAuth2.0 does require more time to configure. For more details on how to authorize using this method, read our guide Using OAuth2.0 in Parabola.

Expiring Access Token

Some APIs will require users to generate access tokens that have short expirations. Generally, any token that expires in less than 1 day is considered to be "short-lived" and may be using this type of authentication. This type of authentication in Parabola serves a grouping of related authentication styles that generally follow the same pattern.

One very specific type of authentication that is served by this option in Parabola is called OAuth2.0 Client Credentials. This differs from our standard OAuth2.0 support, which is built specifically for OAuth2.0 Authorization Code. Both Client Credentials and Authorization Code are part of the OAuth2.0 spec, but represent different Grant Types.

Authenticating with the Expiring Access Token option is more complex than options like Bearer Token, but less complex than OAuth2.0. For more details on how to use this option, read our guide Using Expiring Access Tokens in Parabola.

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Custom Headers

A header is a piece of additional information to be sent with the request to an API. If an API requires additional headers, it is commonly noted in their documentation as -H.

Remember the authentication methods above? Some APIs list the authentication type to be sent as a header. Since Parabola has specific fields for authentication, those headers can typically be ignored.

Taking a look at Webflow's API, they show two headers are required.

The first -H header is linked to a key called Authorization. Parabola takes care of that. It does not need to be added as a header. The second -H header is linked to a key called accept-version. The value of the header is 1.0.0. This likely indicates which version of Webflow's API will be used.

Advanced Settings

Encode URLs

URLs tend to break when there are special characters like spaces, accented characters, or even other URLs. Most often, this occurs when using {text merge} values to dynamically insert data into a URL.

Check the "Encode URLs" box to prevent the URL from breaking if special characters need to be passed.

See sent request

If you woud like to see the request that was sent to the API during the Flow run, you can dothis from the API step. To do this, click the square button next to the Request Settings section in the step to see more detailed information.

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Reading API Errors

Check out this guide to learn more troubleshooting common API errors.

Use the Enrich with API step to make API requests using a list of data, enriching each row with data from an external API endpoint.

Input/output

Our input data has two columns: "data.id" and "data.employee_name".

Our output data, after using this step, has three new columns appended to it: "api.status", "api.data.id", and "api.data.employee_name". This data was appended to each row that made the call to the API.

Custom settings

First, decide if your data needs a GET or POST operation, or the less common PUT or PATCH, and select it in the Type dropdown. A GET operation is the most common way to request data from an API. A POST is another way to request data, though it is more commonly used to make changes, like adding a new user to a table. PUT and PATCH make updates to data, and sometimes return a new value that can be useful.

Insert your API endpoint URL in the text field.

Sending a body in your API request

• A GET cannot send a body in its request. A POST can send a Body in its request. In Parabola, the Body of the request will always be sent in JSON.
• Simple JSON looks like this:

{ "key1":"value1", "key2":"value2", "key3":"value3" }

Using merge tags

• Merge tags can be added to the API Endpoint URL or the Body of a request. For example, if you have a column named "data.id", you could use it in the API Endpoint URL by including {data.id} in it. Your URL would look like this:

http://third-party-api-goes-here.com/users/{data.id}

• Similarly, you can add merge tags to the body.

{
"key1": "{data.id}",
"key2": "{data.employee_name}",
"key3": "{Type}"
}

• For this GET example, your API endpoint URL will require an ID or some sort of unique identifier required by the API to match your data request with the data available. Append that ID column to your API endpoint URL. In this case, we use {data.id}.
• Important Note: If the column referenced in the API endpoint URL is named "api", the enrichment step will remove the column after the calculation. Use the Edit Columns step to change the column name to anything besides "api", such as "api.id".

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Authentication

Most APIs require authentication to access their data. This is likely the first part of their documentation. Try searching for the word "authentication" in their documentation.

Here are the authentication types available in Parabola:

The most common types of authentication are 'Bearer Token', 'Username/Password' (also referred to as Basic), and 'OAuth2.0'. Parabola has integrated these authentication types directly into this step.

Bearer Token

This method requires you to send your API key or API token as a bearer token. Take a look at this example below:

The part that indicates it is a bearer token is this:

-H "Authorization: Bearer sk_test_WiyegCaE6iGr8eSucOHitqFF"

To add this specific token in Parabola, select 'Bearer Token' from the 'Authorization' menu and add "sk_test_WiyegCaE6iGr8eSucOHitqFF" as the value.

Username and Password (Basic)

This method is also referred to as "basic authorization" or simply "basic". Most often, the username and password used to sign into the service can be entered here.

However, some APIs require an API key to be used as a username, password, or both. If that's the case, insert the API key into the respective field noted in the documentation.

The example below demonstrates how to connect to Stripe's API using the basic authorization method.

The endpoint URL shows a request being made to a resource called customers.  The authorization type can be identified as basic for two reasons:

1. The -u indicates a username.
2. Most APIs reference the username and password formatted as username:password. Here, there is a colon with no string following, indicating that only a username is required for authentication.

To authorize this API in Parabola, fill in the fields below:

OAuth2.0

This method is an authorization protocol that allows users to sign into a platform using a third-party account. OAuth2.0 allows a user to selectively grant access for various applications they may want to use.

Authenticating via OAuth2.0 does require more time to configure. For more details on how to authorize using this method, read our guide Using OAuth2.0 in Parabola.

Expiring Access Token

Some APIs will require users to generate access tokens that have short expirations. Generally, any token that expires in less than 1 day is considered to be "short-lived" and may be using this type of authentication. This type of authentication in Parabola serves a grouping of related authentication styles that generally follow the same pattern.

One very specific type of authentication that is served by this option in Parabola is called "OAuth2.0 Client Credentials". This differs from our standard OAuth2.0 support, which is built specifically for "OAuth2.0 Authorization Code". Both methods are part of the OAuth2.0 spec, but represent different grant types.

Authenticating with an expiring access token is more complex than using a bearer token, but less complex than OAuth2.0. For more details on how to use this option, read our guide Using Expiring Access Tokens in Parabola.

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How to work with errors when you expect them in your API calls

Enabling Error Handling

In the Enrich with an API step and the Send to an API step, enable Error Handling to allow your API steps to pass through data even if one or more API requests fail. Modifying this setting will add new error handling columns to your dataset reporting on the status of those API calls

By default, this section will show that the step will stop running when 1 row fails. This has always been the standard behavior of our API steps. Remember, each row of data is a separate API call. With this default setting enabled, you will never see any error handling columns.

Update that setting, and you will see that new columns are set to be added to your data. These new columns are:

• ‍API Success Status
• API Error Code
• API Error Message

API Success Status will print out a true or false value to show if that row's API call succeeded or failed.

API Error Code will have an error code for that row if the API call failed, and will be blank if the API call succeeded.

API Error Message will display the error message associated with any API call that failed, if the API did in fact send us back a message.

With the exception of the default settings, these columns will still be included even if every row succeeded. In that case, you will see the API Success Status column with all true values, and the other two columns as all blank values.

Using the error handling settings

It is smart to set a threshold where the step will still fail if enough rows have failed. Usually, if enough rows fail to make successful API calls, there may be a problem with your step settings, the data you are merging into those calls, or the API itself. In these cases, it is a good idea to ensure that the step can fully stop without needing to run through every row.

Choose to stop running this step if either a static number of rows fail, or if a percentage of rows fail.

You must choose a number greater than 0.

When using a percentage, Parabola will always round up to the next row if the percentage of the current set of rows results in a partial row.

Prevent the step from ever stopping

In rare cases, you may want to ensure that your step never stops running, even if every row results in a failed API call. In that case, set your error handling threshold to any number greater than 100%, such as 101% or 200%.

What to do with these new error handling columns

Once you have enabled this setting, use these new columns to create a branch to deal with errors. The most common use case will be to use a Filter Rows step to filter down to just the rows that have failed, and then send those to a Google Sheet for someone to check on and make adjustments accordingly.

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Error handling in the Live flow Run logs

If you have a flow that is utilizing these error handling columns, the run logs on the live view of the flow will not indicate if any rows were recorded as failed. The run logs will only show a failure if the step was forced to stop by exceeding the threshold of acceptable errors. It is highly advisable that you set up your flow to create a CSV or a Google Sheet of these errors so that you have a record of them from each run.

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Use the Pull from Airtable step to pull in your data from your Airtable databases.

On August 1, 2023, Airtable will no longer allow users to generate new API keys. If you have a Pull from Airtable step that was authorized before July 27th, 2023 (using an API key for authentication), it will continue to pull in data until February 1, 2024. After that date, the step will no longer function. To migrate your step to the new authentication method, open the step, click "Choose Accounts" -> "Add new account". Once that authentication has been added to one step in your Flow, you can switch other Airtable steps to use it as well.

Connect your Airtable account

To connect to your Airtable account, click the blue Authorize button.

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Clicking Authorize will launch a window where you can sign in to Airtable and confirm which bases you would like Parabola to have access to. Any base that you do not select from this menu will not be available to pull data from.

Custom settings

Once connected, you can select the Base, Table and View from your Airtable bases. In the example below, we are pulling data from our Shopify Orders base and our Orders table using the Grid view.

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You can also click Fetch new settings to reload any bases, tables, or views since your data was last imported.

Helpful tips

Linked Records

If your base uses linked records to connect tables, those values will be pulled in as record ids. To get the complete data associated with those records, use another Pull from Airtable step to import the related table. Using the Combine tables step, you can merge the tables together based on a shared record id.

Good to know

If a column has no values in it, that column will not be imported. There must be at least one value present in a row for the column itself to come through.

If a column has a duration in an h:mm format, Airtable exports duration value in millisecond units, parses incoming duration value using minutes. For example, Airtable sends 0:01 as 60.

Use the Send to Airtable step to create, update, or delete records in your Airtable base. Just map the fields in your Airtable base to the related columns in Parabola.

On August 1, 2023, Airtable will no longer allow users to generate new API keys. If you have a Send to Airtable step that was authorized before July 27th, 2023 (using an API key for authentication), it will continue to pull in data until February 1, 2024. After that date, the step will no longer function. To migrate your step to the new authentication method, open the step, click "Choose Accounts" -> "Add new account". Once that authentication has been added to one step in your Flow, you can switch other Airtable steps to use it as well.

Connect your Airtable account

To connect to your Airtable account, click the blue Authorize button.

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Clicking Authorize will launch a window where you can sign in to Airtable and confirm which bases you would like Parabola to have access to. Any base that you do not select from this menu will not be available to pull data from.

Custom settings

Once connected, you can chose to create records, update records, or delete records from the base and table of your choosing.

Creating records

In the example below, we are adding order #2001 to our Orders table within our Shopify Orders base.

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Note how the Airtable fields are displayed on the left-hand side. Each of the columns from your Airtable base appears. On the right-hand side, map the values from our Parabola data to be added into those fields.

Updating records

You can also target a specific record to be updated. Map the Record ID* to the id column in Parabola that contains that data. You can also chose the specific fields you want to update.

In this example, we are updating the Order: Name of record recYmhxVBRqxWNT7N.

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Deleting records

To delete a record, simply map the Record ID* to the id column in Parabola. In this example, we are deleting record recYmhxVBRqxWNT7N.

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Helpful tips

Percentages

Convert your percentages to decimal values using before sending data to Airtable. For example, if your data contains 0.51%, convert that to 0.0051 and adjust your precision values in Airtable. By default, Airtable may interpret that as 0.01%.

Single select/Multi select

You can automatically pass the values of your select options to set those values in your Airtable base. If you enter a select option that does not exist, Airtable will automatically create new select option for that value.

Duration using h:mm format

Airtable parses incoming duration value using minutes. For example, if your duration is 60 milliseconds, Airtable will parse that value as 1:00.

Checkboxes

Set a value of true to toggle a checkbox in your table. Set a value of false to un-toggle a checkbox in your table.

Collaborators

When updating Airtable column with field type collaborator, you can pass in an id or email value. Passing a name value will return an error of "Cannot parse value".

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Use the Pull from Amazon Seller Central step to import reports.

In your flow builder:

1. Drag the Pull from Amazon Seller Central step onto the canvas.
2. Click "Authorize Amazon Seller".
3. A pop-up module will appear prompting you to log into your Amazon Seller Central account.

Configure your settings

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Helpful tips

• This step pulls from Amazon’s Reporting API. If you need data from the Orders or Customers APIs, look for reports that already contain that information.
• There are two types of inventory reports: Inventory and Fulfillment by Amazon (FBA) Inventory. Check both if you’re unsure where your dataset lives. Inventory reports cover products you fulfill directly, while FBA Inventory reports cover products Amazon fulfills on your behalf.
• Amazon’s API can take up to an hour to return report results. Limit the timeframe or data size when possible to reduce wait times.
• The default timezone matches your browser. You can adjust this if needed. Parabola converts your timeframe and timezone to UTC when requesting the report.
• If a report exists in Amazon Seller Central but isn’t available in Parabola, contact us at help@parabola.io.

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The Pull from Box step gives you the ability to pull a CSV or Excel file from your Box account.

Connect your Box account

To connect your Box account to Parabola, select Authorize and follow the prompt to grant Parabola access to your Box files.

Custom settings

Once you have authorized your Box account, select your file in the File dropdown.

Additionally, you can tell Parabola if you're using a different delimiter, such as tab (\t) or semicolon (;), by selecting in the Delimiter dropdown. By default, Parabola will use the standard comma (,) delimiter.

Helpful tips

• Any changes made to the selected Box file will be automatically synced with Parabola the next time you open or run your flow.
• Box's API runs on a 10 minute delay for newly created files. If your newly added Box file does not immediately appear in the File dropdown, check back in a few minutes.

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The Send to Box step gives you the ability to create a new or update an existing file in your Box account.

Connect your Box account

To connect your Box account to Parabola, select Authorize and follow the prompt to grant Parabola access to your Box files.

Custom settings

Select the File dropdown to choose if you want to overwrite an existing file or create a new file.

If creating a new file, give the file a name in the New File Name field.

You can also decide if this is a one-off creation, or if you'd like to create a new file every time your flow runs. If you choose to create a "New File Every Run", each new file will have a timestamp appended to the file name in Box.

Helpful tips

• Anyone with access to an existing file will be able to see and use the changed data.
• Box's API runs on a 10 minute delay for newly created files. If your newly added Box file does not immediately appear in the File dropdown, check back in a few minutes.
• Upload size using our Box step is limited to ~50MB. If you are at or nearing this file size limit, we recommend splitting the file to prevent errors!
• As of today, users cannot specify the destination folder for the Send to Box step.

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Use the Pull from Bubble beta step to retrieve data from your Bubble app.

Connect your Bubble app

Parabola works through Bubble’s Data API, so make sure the Data API is enabled. You can do this in the API section of the settings tab in your Bubble app.

In the Pull from Bubble step, insert your App Name and Object Name in the API Endpoint URL field.

Let's say the thing you want to retrieve from Bubble is "Recipes" — you would replace OBJECTNAME with recipes. The general rule of thumb of here is for the object you want to retrieve, remove the spaces in the name and use lowercase letters! Also, it's worth noting that the Endpoint URL is different if your app isn't live yet (Bubble provides a URL to hit if that's the case). If you need more results, open the advanced settings and increase Max Pages to fetch. You'll also need to add your API Token to the Bearer Token section to authenticate.

To find your API Token, go to Bubble's Settings Tab and generate and copy the token. Paste your API Token into the Bearer Token field and click  Show Updated Results.

Custom settings

The result of the API call is structured in JSON. To flatten within this step, adjust your Top Level Key top level key should be response and 2nd level key should be results.

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Use the Send to Bubble step to send your data to / update your data in your Bubble app.

Connect your Bubble account

Parabola works through Bubble’s Data API, so make sure the Data API is enabled. You can do this in the API section of the settings tab in your Bubble app.

To connect to your Bubble account, you'll need to do so through the Bubble API. The Send to Bubble step pre-fills much of the the information you already need!

When we send data to Bubble, we'll likely use a PATCH or a POST request, which we can use to send data to / update data in your Bubble app. Make sure to update the API Endpoint URL to include your app name and the object you'd like to work with. The general rule of thumb of here is for the object you want to retrieve, remove the spaces in the name and use lowercase letters! Also, it's worth noting that the Endpoint URL is different if your app isn't live yet (Bubble provides a URL to hit if that's the case).

If you need to send data to your endpoint, use the Body field to build the JSON and merge in any cell values by referencing the column name in {curly braces}. In the below example, we show you what it might look like if you wanted to update product inventory in your Bubble app!

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You'll need to add your API Token to the Bearer Token section to authenticate. To find your API Token, go to Bubble's Settings Tab and generate and copy the token. Paste your API Token into the Bearer Token field.

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The Use CSV file step enables you to pull in tabular data from a CSV, TSV, or a semicolon delimited file.

Custom Settings

The first thing to do when using this step is to either drag a file into the outlined box or select "Click to upload a file".

Once the file is uploaded and displayed in the Results tab, you'll see two settings on the lefthand side: File and Delimiter. You can click File to upload a different file. Parabola will default to using a comma delimiter, but you can always update the appropriate delimiter for your file by clicking on the Delimiter dropdown. Comma , , tab \t, and semi-colon ; are the three delimiter types we support.

In the "Advanced Settings", you can set a number of rows and a number of columns to skip when importing your data. This will skip rows from top-down and columns from left-to-right. You can also select a Quote Character which will help make sure data with commas in the values/cells don’t disrupt the CSV structure.

Helpful Tips

• ‍Security: the files you upload through this step are stored by Parabola. We store the data as a convenience, so that the next time you open the flow, the data is still loaded into it. Your data is stored securely in an Amazon S3 Bucket, and all connections are established over SSL and encrypted.
• Limitations: Parabola can't pull in updates to this file from your computer automatically, so you must manually upload the file's updates if you change the original file. Formatting and formulas from a file will not be preserved. When you upload this file, all formulas are converted to their value and formatting is stripped.

The "Generate CSV file" step allows you to export tabular data as a CSV file. You can use it to create custom datasets from various sources within your Flow. Once the Flow run is complete, the CSV file can be downloaded from the Flow’s Run History. You can also configure the step to email a download link to the Flow owner.

Custom Settings

Once you connect your Flow to this export step, it will display a preview of the tabular data to be exported.

The name of the generated file will match the step’s title. To rename your custom dataset file, simply double-click the step title and enter a new name.

After publishing and running your Flow, you can download the generated CSV file from the Flow’s Run History panel. Past CSVs created by this step are also accessible there.

You can optionally configure the step to email a download link to the Flow owner when the run is complete. Please note that this link will expire after 24 hours.

If the step receives zero rows of data as input, no CSV file will be generated and no download link will be emailed.

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Helpful Tips

Security

Files generated by this step are stored by Parabola for your convenience. This allows the data to be reloaded the next time you open the Flow. Your data is stored securely in an Amazon S3 bucket, with all connections established over SSL and encrypted.‍

Limitations

This step supports only one input source at a time.
If your Flow includes multiple branches or datasets, you'll need to connect each one to its own Generate CSV file step to export them separately.

Alternatively, consider using the "Generate Excel file" step, which allows multiple inputs and creates a single Excel file with each input as a separate tab.

Write or paste a sheet of data by hand. Sheets are best used for small datasets like lookup tables or additional rows that can be fed to subsequent steps. This step is limited to 100 rows and 100 columns.

Creating a sheet of data

Create a sheet of data by typing in values, or copying and pasting from an existing spreadsheet. The sheet has 100 rows and 10 columns by default. Extra columns will be added automatically if the data you have pasted requires them. You can also use the "+ Column" button to add more columns manually.

Data can be highlighted across rows, columns, or cells to be edited or deleted. Use the "Clear sheet" button to clear out all data from the sheet, including the headers.

Updates to the dataset will only be saved to be used by other steps in your Flow once you click the "Save this sheet" button.

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Tips

• Max 100 rows and 100 columns
• The first row represents column headers used by subsequent steps
• Sheets don’t accept formulas or formatting, only raw data

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The DHL Shipment Tracking API is used to provide up-to-the-minute shipment status reports by retrieving tracking information for shipments, identifying DHL service providers, and verifying DHL delivery addresses.

DHL is a beta integration which requires a slightly more involved setup process than our native integrations. Following the guidance in this document should help even those without technical experience pull data from DHL. If you run into any questions, shoot our team an email at support@parabola.io.

Use Cases

🤝 DHL | Integration configuration

📖 DHL Reference docs:

https://developer.dhl.com/api-reference/shipment-tracking#reference-docs-section

🔐 DHL Authentication doc links:

https://developer.dhl.com/api-reference/shipment-tracking#get-started-section/user-guide

Instructions

1. Click My Apps on the portal website.

2. Click the + Add App button.

3. The “Add App” form appears.

4. Complete the Add App form.

5. You can select the APIs you want to access.

6. When you have completed the form, click the Add App button.

7. From the My Apps screen, click on the name of your app. The Details screen appears.

8. If you have access to more than one API, click the name of the relevant API.

⚠️ Note: The APIs are listed under the Credentials section.

9. Click the Show link below the asterisk that is hiding the Consumer Key.

🔐 Parabola | Authentication configuration

1. Add an Enrich tracking from DHL step template to your canvas.

2. Click into the Enrich with API: DHL Tracking step to configure your authentication.

3. Under the Authentication Type, select None.

4. Click into the Request Settings to configure your request using the format below:

Request Headers

Example Screenshot

🌐 DHL | Sample API Requests

Track DHL Shipment Statuses by tracking number

Get started with this template.

Test URL

https://api-test.dhl.com/track/

Production URL

https://api-eu.dhl.com/track/

1. Add a Use sample data step to your Flow. You can also import a dataset with tracking numbers into your Flow. (Pull from Excel File, Pull from Google Drive, Pull from API, Use sample data, etc.)

💡 Tip: When using your own data, use the Edit columns step to rename the tracking column in your source data to Tracking Number.

2. Connect it to the Enrich with API: DHL Tracking step.

3. Under Authentication Type, select None.

4. Click into the Request Settings to configure your request using the format below:‍

API Endpoint URL

💡 Tip: The Enrich with API step makes dynamic requests for each row in the table by inserting the tracking number in the API Endpoint URL.

The example above assumes, there is a Tracking Number column and is referenced using curly brackets: {Tracking Number}
Enclose your column header containing tracking numbers with curly brackets to dynamically reference the tracking numbers in your table.

Request Headers

5. Click Refresh data to display the results.

Example Screenshot

📣 Callouts

⚠️ Note: Rate limits protect the DHL infrastructure from suspicious requests that exceed defined thresholds.

When you first request access to the API, you will get the initial service level which allows 250 calls per day with a maximum of 1 call every 5 seconds.

Additional rate limits are available and they are granted according to your specific use case. If you would like to request for additional limits, please proceed with the following steps:

1. Create an app as described under the Get Access section.
2. Click My Apps on the portal website.
3. Click on the App you created.
4. Scroll down to the APIs list and click on the "Request Upgrade" button.

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The Send to Databox step is a beta step. This means that while it's not a fully built-out integration, it's a preconfigured Send to an API step that makes it easy to get set up and send data to Databox using their API.

Databox is a business analytics platform that enables you to pull and analyze all of your data in one place.

The first thing to do is get an API Token from your Databox account. Click here for instructions on how to find your pre-assigned Databox token.

Once you have it, paste the API Token to the Username field on the Send to Databox step. Leave the Password field blank. This is all you need to do to authenticate with the Databox API.

Custom settings

Now, it's time to configure the rest of the Send to Databox step.

When sending data to the Databox API, you will be sending your data row-by-row. Whether you're sending 1 row of data or 500 rows of data, the way you set up the Body field will not change. You can consult Databox's full API documentation here.

Send multiple metrics

When sending multiple metrics to Databox, the Body field of your Send to Databox step should look something like this:

{
  "data":[
     {
        "$clicks":"{Clicks}",
        "date":"{Date}"
     },
     {
        "$sales":"{Sales}",
        "date":"{Date}"
     },
     {
        "$users":"{Users}",
        "date":"{Date}"
     }
  ]
}

The metrics and attributes wrapped in double-quotes " " are the metrics and attributes in Databox. The values wrapped in double-quote and curly braces {} are the column names that store those values in Parabola.

In this example, 3 metrics are being sent, "clicks", "sales", and "users" with its corresponding values store in the columns {Clicks}, {Sales}, and {Users} respectively. "date" is the attribute we're sending for each metric.

The dollar sign $ before a metric name is mandatory. This character is used to differentiate a metric from its attributes.

Send a single metric

When sending a single metric to Databox, the Body field of your Send to Databox step should look something like this:

{
  "data":[
     {
        "$sales":"{Sales}",
        "date":"{Date}"
     }
  ]
}

In this example, 1 metric is being sent, "sales" with its corresponding value stored in the column {Sales}. "date" is the attribute we're sending for that metric.

Send a metric with multiple attributes

When sending a metric with multiple attributes to Databox, the Body field of your Send to Databox step should look something like this:

{
  "data":[
     {
        "$sales":"{Sales}",
        "date":"{Date}",
        "channel":"{Channel}"
     }
  ]
}

In this example, 1 metric is being sent with 2 attributes.  "sales" is the metric being send with its corresponding value in the column, {Sales}, and "date" and "channel" are the attributes with its corresponding values.

Helpful tips

• A metric must be purely numeric. If you have currency values or percentages, send only the number, but set up a proper format for your metric in the Databox Designer. Read how to do that here. You can use the Split column or Extract text from column steps to make your values purely numeric.
• The date, if specified, must confirm to the ISO 8601 standard. Use the Format dates step to get your date values in the required format. Here's a resource you can consult to verify your date format conforms with ISO 8601.

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The Start with date & time row step creates a single row with the current date and time, with customizable offsets by day and timezones. As its name indicates, this step is a starting step so it does not accept any inputs. The current date and time will be determined automatically at the time the flow is run.

You would start your flow off with the Start with date & time row if you need relative date data as one of your data sources. The most common use for this step is if you need to provide date variables when working with APIs. Many APIs require dates to be sent in order to pull the information that you need. Since manually adjusting those dates before every flow run would defeat the purpose of an automation, this Start with date & time row solves for that.

Customize settings

You can add multiple rules to this step by clicking on the blue Add Date & Time Rule link. Each rule will be represented in a new column.

By default, the Days Offset field will be set to 0, meaning the date displayed is the current date and time. If you choose a positive value for the offset, it will display a future date, and if you choose a negative value, it will display a past date.

All date and time values created by this step look like this: 2019-09-18 12:33:09 which is a format of YYYY-MM-DD hh:mm:ss. If you prefer a different date format, connect a Format dates step right after this one to get the date values in your preferred format.

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Use the Send to DocSpring step to automatically create submissions for your DocSpring PDF Templates.

Connect your DocSpring account

To connect to your DocSpring account, you'll first need to click the blue "Authorize" button.

You'll need your DocSpring API Token ID and your DocSpring API Token Secret to proceed. To do so, visit your API Token settings in DocSpring.

Reminder: if you're creating a new API Token, the Token Secret will only be revealed immediately after creating the new Token. Be sure to copy and paste or write it down in a secure location. Once you've created or copied your API Token ID and Secret, come back to Parabola and paste them into the correct fields.

Custom settings

To pull in the correct DocSpring Template, you'll need to locate the Template ID. Open the Template you want to connect in DocSpring and locate the URL.  The Template ID is the string of characters following templates/ in the URL:

https://app.docspring.com/templates/{Template ID}

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Paste the ID from the URL in the Template ID field.

Helpful tips

• Your PDF templates in DocSpring can accept a variety of data types to fill in their fields, however, there are no column mapping options in Parabola. Make sure your column headers match the names of the fields in DocSpring, exactly, to ensure your data fills in the correct predefined field in the PDF.

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The Pull from Drip step is a beta step. This means that while it's not a fully built-out integration, it's a preconfigured Pull from an API step that makes it easy to get set up and pull data from Drip using their API.

Drip is a marketing automation platform built for ecommerce.

Connect to your Drip account

You will need the following 3 things to connect to the Drip API:

• Your Account ID
• Your API Key
• A name to call the connection between Parabola and Drip (this can be anything)

You should be able to locate your API Key from your User Settings page on Drip.

Once you've located this information from Drip:

1. Add your account ID to the API Endpoint URL where specified
2. Add your API Key to the Username field. Please keep the Password field blank.
3. Add the name you thought of to the User-Agent header value. This can be anything, but you do need to put something.

Custom settings

By default, the Pull from Drip beta step is set up to pull data from the Subscribers API endpoint which pull a list of all subscribers.

https://api.getdrip.com/v2/YOUR_ACCOUNT_ID/subscribers

You can update that endpoint URL in the API Endpoint URL field if you'd like to pull in other data from Drip's API. You can read their full API docs here.

Helpful tips

• If the Pull from Drip step does not bring back all of your data, try increasing the "Max Pages to Fetch" field so that more pages are fetched.

The Pull from Dropbox step gives you the ability to pull in a spreadsheet from your Dropbox account. You can pull in CSV and XLS files from a personal or shared folder.

Connect your Dropbox account

To connect to your Dropbox account, select Authorize to login in with your Dropbox credentials.

Custom settings

To pull a Dropbox file into Parabola, select it from the File dropdown. You will see all files that you have access to (for Dropbox Business customers, that means both personal and team files).

If your file is a CSV, you can then choose the Delimiter. By default, the delimiter is set to comma , , but you can also select tab \t or semicolon ; to match your data source.

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Helpful tips

• Any changes you make to the Dropbox file will be automatically synced with Parabola.
• This step will only be able to pull in files that are located in directories that are owned by the primary owner of the Dropbox account.
• For Dropbox Business accounts, you can pull in files from either team folders or your private member folder. You will see a combined list of all files you have access to.
• To search for a specific file, enter a search term in "Search by filename" (under Advanced Settings). Click to show updated results, and any matching files will now appear in the File dropdown.

The Send to Dropbox step gives you the ability to send CSV data to your Dropbox account. You can choose between creating a completely new file, once or everytime the flow runs, or updating an existing file in Dropbox.

Connect your Dropbox account

To connect to your Dropbox account, click Authorize to login in with your Dropbox credentials.

Custom settings

Under the File dropdown, decide if your data will create a brand new file or overwrite an existing file that already exists in Dropbox. When overwriting an existing file, you will see all files you have access to (for Dropbox Business customers, that means both personal and team files).

If you select to Create New File, you must also give your file a New File Name.

You can toggle New File Every Run, which when turned off, gives you the ability to send this newly created file is a one-off, or if turned on, will create a separate, new file in Dropbox each time the flow runs.

Helpful tips

• Anyone with access to the existing file will be able to see and use the changed data.
• For Dropbox Business accounts, new files will automatically be created in your private member folder.
• To search for a specific file, enter a search term in "Search by filename" (under Advanced Settings). Click to show updated results, and any matching files will now appear in the File dropdown.

The Email a file attachment step gives you the ability to send an email to a list of recipients with a custom message and an attached file (CSV or Excel) of your transformed data.

Setup

After connecting your flow to this destination step, enter up to ten email addresses in Email Recipients section. Enter what you'd like the email subject line to be in the Email Subject section. Enter your custom message in the Email Body section. Please note that all of these fields are required.

You can use merge tags {} to include dynamic values in any field (recipients, subject, body, file name, reply to). Those work as follows:

1. Wrap the column name that you want to reference in tags - in the example below, that could be "{Name}"
2. The email will then take the first row from that column, from the first input connected
3. The step can filter out any columns used as merge columns. You can find this option under Advanced Settings

In the "Advanced Settings" dropdown, enter the email address you wish for recipients to reply to. This will ensure that any replies to these emails will go to the right place.

The step can accept multiple input arrows of data if it is set to generate an Excel file. Each input will be a new tab within the generated file, and each tab must be given a unique name.

Security‍

‍The files you send through this step are stored by Parabola. We store the data as a convenience, so that the next time you open the flow, the data is still loaded into it. Your data is stored securely in an Amazon S3 Bucket, and all connections are established over SSL and encrypted.

Limitations

• Emails sent through this step must be 30MB or smaller.
• An email is not sent if there are zeros rows of input data to send.
• This is different from the "Generate CSV file" or "Generate Excel file" steps, which create a downloadable file and email a link (not an attachment) to the Flow owner. Files from the "Generate" steps can also be accessed in the Flow's Run History, while the "Email a file attachment" step is designed to deliver attached files to any email address you specify.

Extract from email can pull in data from a number of filetypes, including attached PDF files. Once configured, Parabola can be set to parse PDFs anytime the relevant email receives a PDF file.

Step configuration instructions can be found here.

Pull from file queue - PDF files

Pull from file queue can receive PDF files and parse the relevant data. The file queue is a way to enqueue a Flow to run with a series of metadata + a file that is accessible via URL.

Runs can be added to the file queue via API (webhook) or via Run another Parabola Flow.

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The Extract from email step gives you the ability to receive file attachments (CSV, XLS, PDF, or JSON files) from an incoming email and pass it to the next step (eg., combining email data with PDF or Google Sheets data). The step also gives you the ability to pull an email subject and body into a Parabola Flow. Use this unique step to trigger Flows, using content from the email itself.

Watch the Parabola University video below to see this data pull in action.

Default attachment settings

To begin, take note of the generated email address that is unique to this specific flow. Copy the email address to your clipboard to start using this dedicated email address yourself or to share with others.

The File Type is set to CSV / TSV, though you can also receive XLS / XLSX, PDF, or JSON files.

The Delimiter is set to comma (,), but can also be adjusted to tab (\t) and semicolon (;). If needed, the default of Quote Character set to Double quote ( " " ) can be changed to single quote ( ' ' ).

Custom settings

This step contains optional Advanced settings, where you can tell Parabola to skip a certain number of rows or columns when receiving the attached file.

Auto-forwarding emails into a Parabola flow

To auto-forward a CSV attachment to an email outside of your domain, you may need to verify the @inbound.parabola.io email address. The below example shows how to set this up in Gmail.

Video overview

Step-by-step instructions

1. Prepare Your Extract from Email Step in Parabola

1. In your Parabola Flow, drag in a new Extract from Email step.
2. Configure it to pull in email content, not just attachments.
3. Click Update Results to generate the Parabola forwarding email address.

💡 You’ll use this address to forward emails into your Parabola Flow. Don't forget to copy this email address.

2. Set Up Forwarding in Gmail

1. Go to Gmail → click the gear icon → See all settings.
2. Navigate to the Forwarding and POP/IMAP tab.
3. Click “Add a forwarding address.”
4. Paste the email address from your Parabola step and click Next → Proceed.

3. Confirm the Gmail Forwarding Request via Parabola

1. Back in Parabola, refresh the Extract from Email step.
2. Look for an email with subject: Gmail Forwarding Confirmation.
3. Open the body and find the sentence:
   “Please click the link below to confirm the request.” It maybe easier to copy and paste the entire body content into a word doc or text editor for easier copy and pasting.
4. Copy the confirmation URL from the body, paste it in a browser, and click Confirm.

✅ Gmail will now recognize the Parabola address as a valid forwarding destination.

4. Create a Gmail Filter to Automatically Forward Specific Emails

1. In Gmail, go to Settings → Filters and Blocked Addresses → Create a new filter.
2. Set criteria such as:
   • From: nycwarehouse@gmail.com
   • Subject: New York City Warehouse Inventory
   • Has attachment: ✅
3. Click Create filter, then:
   • Check Forward it to and select your verified Parabola email address.
   • Click Create filter.

5. Clean up your Flow (If necessary)

1. If you created a temporary Extract from Email step just for the verification, you can now delete it.
2. Your Parabola Flow will continue to receive the filtered, auto-forwarded emails daily.

Other troubleshooting tips

• If you do not see the email content come into the Flow after a few minutes, double-check the email settings on that step/Flow. Click on the gear icon in the lefthand side of the step where it says "View all Flow email settings". Make sure the checkbox "Reject emails that do not contain valid attachments" is unchecked.
• If it is already checked, check your email inbox for an email with the subject line, "Sorry, we were unable to process your email attachment". The verification link from gmail should be available in the email content of this email. Click on the verification link and you should have successful verified this forwarding address!

Pull multiple file attachments

By default, Flows will run with the first valid attached file. If you want the Flow to run through multiple attached files (multiple attachments on one email), open the “Email trigger settings” modal and change the setting to “Run the Flow once per attachment:”

(Access these settings from the Extract from email step, or from the Flow trigger settings on the published Flow page.)

For emails with multiple files attached, the Flow will run once per file received, sequentially.

• Files must be of the same type (CSV, XLS, PDF, or JSON) for the runs to process.
• The file type is defined in the initial step settings (”File type” dropdown).
• Any files received that are of a different type will cause a Flow run error.

Pull from email content

We also support the ability to pull in additional information about an email.The default behavior pulls:

• Subject
• Body (plain text)
• CC
• From
• Attached file name

Additional fields:

• Body (HTML)
• Body (all URLs)
• Attached file URL

To access these fields, you can toggle the “Pull data from" field to ‘Email content’. If you'd like to pull both an attachment and the subject and body, select ‘Email content and attachment’.

Extract data from the body of an email with AI

Use the “Extract data with AI” option to automatically extract tables and key values from email bodies to create structured output.

Enable this option under "Parsing settings" when pulling in the “Email content”.

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Pull a sheet from an Excel file based on file position

Use the "position is" option when pulling in an attached Excel document to specify which sheet to pull data from by its position, rather than its name. This is great for files that have key data in consistent sheet positions, but may not always have consistent sheet names.

When using this option, only the number of sheets that are in the last emailed file will show in the dropdown. If a Flow using these settings is run and there is no sheet in the specified position, the step will error.

Helpful tips

• This step will run every time the dedicated email address receives a new attached file. This is useful for triggering your flow to run automatically, outside of a dedicated schedule or webhook.
• If your XLS file has multiple sheets, this step auto-selects the first sheet but can be set to look for a specific sheet.
• This step can handle attached files that are up to 5MB.
• Each run of a Flow uses one file. If your Flow has multiple Extract from email steps, they will all access the same email / file.
• What happens when multiple emails are received by your flow: If your flow is processing and another email (or multiple) comes in, then they will queue up to be pulled into your flow in the order they were received. All emails sent to a flow (up to 1,000 total) will be queued up and processed.
• By default, emails that are sent to Flow email addresses must have a valid attachment. You can disable that, and allow emails without attachments, by accessing the email trigger management modal and disabling the checkbox.
• This step can only ingest data from an email, not download a file. To generate and download a CSV from a link in an email, take the following steps:
  
  • Extract the CSV’s URL from the email content using Extract from email
  • Pass the URL into a Run another Flow step at the end of the Flow
  • Begin your destination Flow with Pull from file queue
  • End the destination Flow with a Generate CSV file step

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Use the "Extract data with AI" option to extract tables of data and individual values from messy and difficult excel files.

Understanding your Excel data

When extracting data from an Excel file, use the settings to extract a table, or individual values (or both)

• Tables should be composed of columns and rows, with a row representing the names of the columns
• Individual values are single pieces of data that are applicable to the entire document. For example, a date at the top of a document or an invoice number
• Columns and individual values can be given additional information to ensure the tool is identifying and returning the correct information - more on that below!

Step Configuration

Selecting Excel extraction

Once you have an Excel file in your flow, select "Extract data with AI". You will see options to add details to "Extract a table" and/or "Extract individual values".

Clicking on either of those will show additional fields to fill out. Each step can extract 1 table and any number of individual values.

Extract a table

Once you enable table extraction, do the following:

1. Give your table a description - this is used by AI to find the table so it's important to be clear and precise, especially if many tables are present.
2. Define your columns - each column can be named, given example values, and additional instructions. If a column is conceptually clear (i.e. "Item description") then a name might be all you need. But if the name of the column is ambiguous, or its values are ambiguous, it is best practice to add example cell values, as well as additional instructions describing what this column represents and how an AI should find it.

Extract individual values

Once you enable individual value extraction, do the following:

1. Define your value - each value can be named, given example values, and additional instructions. If a value is conceptually clear (i.e. "Port of entry") then a name might be all you need. But if the name of the column is ambiguous, or its values are ambiguous, it is best practice to add example cell values, as well as additional instructions describing what this value represents and how an AI should find it.

Choosing the "type" for a column or individual value

Columns and individual values are Text by default. But you can change that to improve accuracy:

• Text - anything
• True / False - results in either "True" or "False", can be used to detect checkmarks and other indicators
• Number - will remove trailing zeros on any number
• Currency -converts the currency to a number
• Date - uses "2022-09-27T18:00:00.000" format
• Signature - converts signatures to text
• List of options - chooses from a list of possible options you provide

Check out this Parabola University video for a quick intro to our PDF parsing capabilities, and see below for an overview of how to read and configure your PDF data in Parabola.

Understanding your PDF data

Parabola’s Pull from PDF file step can be configured to return Columns or Keys

• Columns are parts of tables that are likely to have more than one row associated with them
• Keys are single pieces of data that are applicable to the entire document. As an example - “Total” rows or fields like dates that only appear once at the top of a document are best expressed as keys
• Sometimes AI can interpret something as a column or a key that a human might consider the other. If the tool is not correctly pulling a piece of information, you might try experimenting with columns versus keys for that data point
• Both columns and keys can be given additional information from you to ensure the tool is identifying and returning the correct information - more on that below!

Step Configuration

You can use Extract from PDF, Extract from email, and Pull from file queue to parse PDFs. Once you have a PDF file uploaded into your Flow, the configuration settings are uniform.

Extract a table

1. Auto-detected Table (default)
Parabola scans your PDF, detects possible tables, and labels the most likely columns. This option uses LLM technology and works exceptionally well if the PDF document has a clear, structured table. All detected tables will be available in the sub-dropdown under the "Use an auto-detected table" dropdown.

• Quickest setup
• Works best when your table has headers
• You can manually add more columns or keys after

2. Define a Custom Table
‍Manually define the structure of your table if the AI didn’t pick it up. You can name the table and define the columns that you want to extract from the PDF by clicking on the + Add Column button.

• Good for multi-table documents
• Works well with tables spread across multiple pages
• Requires a bit more setup

3. Extract All Data (OCR-first mode)
Use OCR to return all text from the PDF — helpful if the structure is complex or you're feeding the result into an AI step later. We only recommend this option if the first two extraction methods aren't yielding the desired results.

Return formats:

• All data → Every value, one per row
• Table data → Tables split by page, each with a table ID
• Key-value pairs → Labeled items like SKU: 12345
• Raw text → One cell per page, useful for follow-up AI parsing

Extract values

If there are document-level values like invoice date and PO number that you want to extract, add them as keys in this section. You can add this by clicking on the “+ Add key” button. Each key that you configure will be represented as its own column and the value will be repeated across all the rows of the resulting data set.

• Column and key names can be descriptive or instructive, and do not need to match exactly what the PDF says. However, you should try to ensure the name is something that the underlying AI can associate with the desired column of data
• Providing examples is the best way to increase the accuracy of column (or key) parsing
• The “Additional instructions to find this value” field is not required, however, here you can input further instructions on how to identify a value as well as instructions on how to manipulate that value. For example in a scenario where you want to make two distinct columns out of a singular value in the file, say an order number in the format “ABC:123".  You might use the prompt - “Take the order ID and extract all of the characters before the “:” into a new column”

See below how in this case with handwriting, with more instructions the tool is able to determine if there is writing next to the word “YES” or “NO”.

Fine Tuning

You can give the AI more context by typing additional context and instructions into this text box. Try using specific examples, or explain the situation and the specific desired outcome. Consult the chat interface on the lefthand side to help you write clear instructions.

Advanced Settings

1. Text parsing approach
You can specify the text parsing approach if necessary. The default setting is “Auto” and we recommend keeping it this way if possible. If it’s not properly parsing your PDF, you can choose between “OCR” and “Markdown”.

• OCR - This will use a more sophisticated version of OCR text extraction that can be helpful for complex documents such as those with handwriting. This more advanced model may, however, result in the tool running slower.
• Markdown - This will use Markdown for parsing. It is generally faster for parsing and may work better for certain documents, like pdfs that have nested columns and rows.

2. Retry step on error
The checkbox will be checked by default. LLMs can occasionally return unexpected errors and oftentimes, re-running the step will resolve the issue. When checked, this step will automatically attempt to re-run one time when encountering an unexpected error.

3. Auto-update prompt versions
The checkbox will be unchecked by default. Occasionally Parabola updates step prompts in order to make parsing results more accurate/reliable. These updates may change output results, and as a result, auto-updating is turned off by default. Enable this setting to always use the most reset prompt versions.‍

4. Page filtering
The checkbox will be unchecked by default. This setting allows users to define specific pages of a document to parse. If you only need specific values that are consistently on the same page(s), this can drastically improve run time. If you do check this box off, please make sure to complete the dropdown settings that appear below.

• Keep, Remove, or Autodetect
  
  • The Autodetect option will allow the parser to choose what pages to use.

• The first, the last, or these
  
  • If you select “the first”, input a number in the “#” box to instruct how many pages from the beginning of the file should be parsed.
    
  • If you select “the last”, input a number in the #” box to instruct how many pages from the end of the file should be parsed.
  • If you select “these”, input a comma-separated list of numbers in the blank box to specify which pages. For example, if you put “1, 10, 16”, the step will parse the first, tenth, and sixteenth page only of the file.

Usage tips & Other Notes

• The more document pages that are needed for parsing, the longer it may take. To expedite this process, you can configure the step to only review certain pages from your file. The fewer the pages, the faster the results!
• If you need to pull data across multiple tables (from a single file), you will likely need multiple steps – one per table.
• File size: PDF files must be <500 MB and 30 pages
• PDFs cannot be password protected
• We recommend always auditing the results returned in Parabola to ensure that they’re complete

Using child columns

Mark columns as “Child columns” if they contain rows that have values unique from the parent columns:

Before:

After marking “Size” as a child column:

The Use Excel file step enables you to pull in tabular data from an Excel file.

Custom settings

First, select Click to upload a file.

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If your Excel file has multiple sheets, select which one you'd like to use in the dropdown menu for Sheet.

In the Advanced Settings, you may also select to skip rows or columns. This will skip rows from top-down and columns from left-to-right.

Formatted data

Cell data is imported as formatted values from Excel. Dates, numbers, and currencies will be represented as they appear in the Excel workbook, as opposed to their true underlying value.

Enabling unformatted values will import the underlying data from Excel. Most notably, this will show raw numbers without any rounding applied, and will convert dates to Excel's native date format (the number of days since 1900-01-01).

Helpful tips

Limitations

This step can't pull in file updates from your computer, so if you make dataset changes and wish to bring them into Parabola, this requires manually uploading the updated Excel file. When you upload an Excel file, all formulas are converted to their value, and formatting is stripped (formatting or formulas are not preserved). If you want to pull in live updates on each run without having to upload a file manually, you can use a step like Pull from SharePoint, OneDrive, or Google Drive.

Security

The files you upload through this step are stored by Parabola. We store the data as a convenience, so that the next time you open the flow, the data is still loaded into it. Your data is stored securely in an Amazon S3 Bucket, and all connections are established over SSL and encrypted.

Custom Settings

Once you connect your Flow to this export step, it will show a preview of the tabular data to be sent.

The step will automatically send this downloadable Excel file link to the email address of the Flow owner.

By default, the name of the file will be ‘Parabola Excel File’—if you'd like to rename your dataset, click the box under ‘Download a Excel file named’ and type your new filename.

Note that the Generate Excel file step can take multiple inputs. Each input step will send data to a separate sheet, and the names of these sheets can be customized. 'Input 1' will map to 'Sheet 1' by default, and so forth. Refer to the 'Input' tabs at the top of your step window to ensure your step is sending your data to the desired source.

Once you publish and run your Flow, the emailed Excel file link will expire after 24 hours.

If the step has no data in it (0 rows), then even after running your Flow, an email with an Excel file won't be sent.

You can download past Excel files that were generated with this step by opening the “Run History” panel, navigating to the Flow run, and clicking Download Excel.

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Helpful Tips

Security

The files you send through this step are stored by Parabola. We store the data as a convenience, so that the next time you open the Flow, the data is still loaded into it. Your data is stored securely in an Amazon S3 Bucket, and all connections are established over SSL and encrypted.‍

Limitations

All sheet names must be less than or equal to 31 characters, or the Flow will fail.

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Pull in files from an FTP, SFTP, or FTPS server. Target exact files, or find files based on rules within a folder. Supports csv, tsv, excel, xml, and json file parsing. Can parse edi files as csv.

Connecting to your FTP server

The first thing that you need to do is connect to your server in order to pull in any files.

When you first add an FTP step to a flow, you can open it and will see an Authorize button.

Click Authorize, and you will see this form:

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You will need to fill in each field in order to connect.

The Port can be manually set, or it will default to a port depending on which choice you have selected for the transfer protocol.

Using FTP (instead of SFTP or FTPS) is not recommended. Most FTP servers offer one of the other options.

If you are connecting via SFTP and are using a private key to connect, you will need to check the "Use public key authentication" box to see the option to upload that key and connect.

Editing your FTP connection settings or adding another connection

If you need to edit or add another connection, open your FTP step, click on "Select accounts", and then either click to add a new account, or edit the existing one.

After editing your connection settings, click the refresh button to have the step re-connect with the new settings.

Pulling in a specific file

The main option at the top of the step allows you to switch between pulling in a specific file and a file based on rules.

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When pulling in a specific file, enter the path to that file. All paths should start with / and then progress through any folders until ending in the name of the file and its extension.

Click the 3-dot more menu to override how to parse a file. By default, this step will parse the file based on the extension of the file. But you can change that. For example, if you have a .txt file that is really a csv file inside, you can choose to parse that txt file as if it were a csv.

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Pulling in a file based on rules

The main option at the top of the step allows you to switch between pulling in a specific file and a file based on rules.

When pulling a file based on rules, a new file will be pulled in every time the flow is run, or the step is refreshed.

A file can be selected based on:

• The last_modified date of the file
• The folder the file is in
• Matching part of the name of the file

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First, choose between pulling the newest file or the oldest file, based on its last modified date.

Second, choose a file name pattern. If you select is anything, no filtering based on file name will be applied. You can select to filter for files that start with, end with, or contain a certain set of characters. This can also be used to match the file extension (.csv for example).

Third, choose a folder to find the file within. If you use / then it will search the root folder. Other folders that are inside of the folder that you have indicated will not be searched and will be ignored.

Finally, select a parsing option if you want to override the default.

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Every time a file is pulled in from a rule, the name will be displayed in the step settings.

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Moving files after processing (archiving)

Enable the Archive file once processed setting to automatically move files from the target folder to a different folder.

Files will be moved immediately after the data from the file is fetched by the Pull from FTP step. If the step fails for some reason with an error, the file will not be moved.

If the file is pulled in successfully, but another step causes the Flow to fail, then the file will still be archived, even if the overall Flow failed to complete.

In the run history of the Flow, the names of any files pulled in from FTP will be listed to show what file was moved during successful or failed runs.

Use of this setting is best combined with the “Pull in a file based on rules” setting. With this combination, a Pull from FTP step can continuously cycle through a specific FTP folder and process any files available within it.

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Parsing XML Files

Sometimes XML files will not successfully pull into this step. In that case, it may be due to how the step is parsing the file by default. Use the Top Level Key field to indicate which key to expand into the table. This can help if there is a list of data, but there are other keys surrounding it, and you just need to get to that interior list. You can indicate a deeper key by placing dots between each key level. For example, if you have an object called Cars, and inside it is a list called Colors, which you want to expand, you would put Cars.Colors in the Top Level Keys field.

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‍File Metadata

To pull in file metadata like the date the file was last modified, it must be included as a value in a column.

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Limits

This FTP step can be used to pull in files up to 600MB. Contact us if you need larger files to be pulled in.

Global limits my stop your file before its size does, however. Steps can only run for 1 hour, and can only pull in 5 million rows and 5000 columns.

Create or overwrite files in an FTP, SFTP, or FTPS server. Supports CSV, TSV, Excel, and JSON file creation and overwriting.

Connecting to your FTP server

The first thing that you need to do is connect to your server in order to send any files.

When you first add an FTP step to a flow, you can open it and will see an Authorize button.

Click Authorize, and you will see this form:

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You will need to fill in each field in order to connect.

The Port can be manually set, or it will default to a port depending on which choice you have selected for the transfer protocol.

Using FTP (instead of SFTP or FTPS) is not recommended. Most FTP servers offer one of the other options.

Editing your FTP connection settings or adding another connection

If you need to edit or add another connection, open your FTP step, click on "Select accounts", and then either click to add a new account, or edit the existing one.

After editing your connection settings, click the refresh button to have the step re-connect with the new settings.

Creating a new file each time

The main option at the top of the step allows you to switch between creating a new file, and overwriting a file.

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When creating a new file, you have a few settings to fill out:

• The format of the file
• The name of the file
• What folder to place the file within.

JSON Files

JSON files generated have their array as the top level element. Each row will be converted into an object, and then each row-object will be comma separated in the top level array.

Given data in Parabola that looks like this:

You can expect JSON that looks like this:

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Excel Files

Excel files that are created by this step are in the .xlsx format. They will have no additional formatting applied.

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‍CSV Files

You have the option to remove the header row when generating a CSV file. CSV files generated by this step will have the .csv format.

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Naming a file

In the field for the name of your file, you can type anything that you'd like to name your file. Do not include the extension, as one will be automatically added by the step, according to the format you have chosen.

If you put "my file.csv" in the file name field, and then have the step create a CSV file, it will ultimately be named "my file.csv.csv" in your FTP server.

Most servers will not be happy if you try to name a file, and that name already exists in that folder. To get around this, you can use merge tags to add dates and time to your file name. Anywhere you place that tag in the name field, the date of the run will be inserted in the following formats:

• {date} YYYY-MM-DD
• {dateNoDash} YYYYMMDD
• {dateTime} YYYY-MM-DD HH:MM:SS
• {timestampDash} YYYY-MM-DD_HH-MM-SS

All dates and times are in UTC timezone.

Selecting a folder to put the file in

The final setting is used to indicate where the file should go.

The root of your server will be at / and any other folder will start with / as well. If you have a folder named "reports" that is located in the Root folder, then you would use /reports in the folder field.

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Overwriting a file

The main option at the top of the step allows you to switch between creating a new file or overwriting a file.

Overwriting a file is simple - enter the path to the file to overwrite each time, and the format for the new data inside that file.

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Selecting a format

It is best to select the format of the file that it's extension indicates. Because the data is fully replaced within the file, the format that Parabola sends does not strictly need to match the format that the name of the file indicates.

For example, you could send CSV data to a file named jobs.txt and it would work fine. But having an extension on a file that does not represent how it should be used or read can cause issues down the line.

Selecting a file to overwrite

The final setting is used to indicate the path to the file to overwrite.

Paths should always start with a / which is the root folder. From there, you can add more folders (or not), and end with the file name and its extension.

In the image above, we are targeting a file named customers.csv which is in the root folder. If that file was in a sub folder named crm, then the path would look like this:

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Use the Pull from Facebook Ads step to connect to any Facebook Ads account and pull in custom reports or columns and breakdowns.

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Connect your Facebook Ads account

Double-click on the reviewing in webflow step and click the blue button to "Login with Facebook". A pop-up window will appear asking you to log in to your Facebook account to connect your data to Parabola.

If you ever need to change the Facebook Ads account that your step is connected to, or connect to multiple Facebook Ads account within a single flow, click "Edit accounts" at the top of the step. Head here for more info.

Default settings

The default settings for this flow will allow you to see data from your Facebook Ads account right away. If you have multiple Ads accounts, be sure to select the correct account here:

By default, the step will pull in insight for the last 7 days.

Selected columns:

• Reach
• Frequency
• Impressions
• Amount Spent
• CPM (Cost per 1,000 impressions)
• Link Clicks
• CTR (Link Click-Through Rate)
• CPC (Cost per Link Click)

Selected breakdown:

• Campaign Name
• Ad Set Name
• Ad Name

Presets

We've added a lot of standard reports that Facebook Ads shows in their Ads Manager page. Selecting a standard report will update your Columns and Breakdowns selection fields to show the columns that will be imported.

These standard reports can be used as it, or can also start as a great starting point to further customize your report.

Available standard reports:

• All Levels
• Campaign
• Ad Set
• Ad
• Objective
• Age
• Gender
• Age and Gender
• Country
• Region
• DMA Region
• Business Locations
• Placement
• Placement and Device
• Platform
• Platform and Device
• Time of Day (Ad Account)
• Time of Day (Viewer)
• Canvas Component
• Conversion Device
• Product ID
• Carousel Card
• Reactions
• Video View Type
• Video Sound

Custom settings

To further customize your Facebook Ads data being pulled into Parabola, you can select Columns and Breakdowns.

Each breakdown will also add its own column, and break each row into multiple rows. For example, you could look at your Reach column, and break it down by Campaign to see the reach of each campaign

Custom date ranges

You can either select a preset relative date or a custom date range in this step.

Select a preset relative date range, such as the Last 7 Days, to pull data from a range that will update every time this flow runs.

Select a custom period between, such as September 17, 2020 - September 24, 2020 to pull from a static date range that will always pull from that set range when the flow runs.

At the bottom of the step, we'll display the attribution window that is being used to product your report:

Using a 28-day click and 1-day view attribution window in your Facebook account's time zone.

Your Facebook account time zone will be used to determine how to pull data from your selected date range.

Known issues

Currently there is a known issue in the Facebook API that has not been resolved by their team yet. It causes certain requests to timeout or error when they should work. Our team is keeping tabs on the issue and will remove this known issue when it has been fixed by Facebook. In the meantime, you may need to remove certain columns of breakdowns from your settings in order to get the step working and returning data!

Helpful tips

• This step is available starting at our Plus plan.

The FedEx API is used by businesses, developers, and logistics managers to integrate FedEx's shipping, tracking, and logistics services into their platforms and operations.

FedEx is a beta integration which requires a slightly more involved setup process than our native integrations. Following the guidance in this document should help even those without technical experience to enrich data from FedEx. If you run into any questions, shoot our team an email at support@parabola.io.

🤝  FedEx | Integration configuration

📖  FedEx API Reference:
‍https://developer.fedex.com/api/en-us/get-started.html

🔐  FedEx Authentication Documentation:https://developer.fedex.com/api/en-us/catalog/authorization/v1/docs.html

Instructions

1. Navigate to the FedEx Developer Portal.

2. Click Login to access your FedEx account.

3. In the side-menu, select My Projects.

4. Click + CREATE API PROJECT.

5. Complete the modal by selecting the option that best identifies your business needs for integrating with FedEx APIs.

6. Navigate to the Select API(s) tab.

7. Select the API(s) you want to include in your project. Based on the API(s) you select, you may need to make some additional selections.

⚠️ Note: If you select Track API, complete the additional steps below:

1. Select an account number to associate with your production key.
2. Review the Track API quotas, rate limits, and certification details.
3. Choose whether or not you want to opt-in to emails that will notify you if you exceed your quota.

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8. Navigate to the Configure project tab.

9. Configure your project settings with name, shipping location, and notification preferences.

10. Navigate to the Confirm details tab.

11. Review your project details, then accept the terms and conditions.

12. On the Project overview page, retrieve your API Key and Secret Key.

💡 Tip: Use Production Keys to connect to live production data in Parabola. Use Test Keys to review the request and response formats using from the documentation.

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13. Configure an Expiring Access Token request to the OAuth endpoint in Parabola.

🔐  Parabola | Authentication configuration

1. Add an Enrich tracking from FedEx step template to your canvas.

2. Click into the Enrich with API: FedEx Tracking step to configure your authentication.

3. Under the Authentication Type, select Expiring Access Token before selecting Configure Auth.

4. Enter your credentials to make a request to the OAuth endpoint using the format below:

Sandbox URL‍

https://apis-sandbox.fedex.com/oauth/token

Production URL

https://apis.fedex.com/oauth/token

Request Body Parameters

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⚠️ Note: Use your API Key in place of your Client ID. Use your Secret Key in place of your Client Secret.

Request Headers

Response Access Token Field

access_token

5. Click Advanced Options

Header Key for Using Access Token

Authorization

Header Value for Using Access Token

Bearer {token}

6. Click Authorize

Example Screenshot

Track FedEx shipment statuses by tracking number

Get started with this template.

1. Add a Use sample data step to yourFlow. You can also import a dataset with tracking numbers into your Flow (Pull from Excel File, Pull from Google Drive, Pull from API, etc.)

💡 Tip: When using your own data, use the Edit columns step to rename the tracking column in your source data to Tracking Number.

2. Connect it to the Enrich with API: FedEx Tracking step.

3. Under Authentication Type, select Expiring Access Token to use your authentication credentials.

4. Click into the Request Settings to configure your request using the format below:

API Endpoint URL

Request Body

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Request Headers

5. Click Refresh data to display the results.

Example Screenshot

The Pull from file queue receives a file URL (CSV, PDF, Excel) along with associated data. Use this to trigger Flows to process a file via a URL that is sent to the Flow.

This step is currently offered to users on our Advanced Plan. Check out the Pricing Page for additional information.

Sending a file to your Parabola Flow

The file queue processes files that are accessible via URL. To send a file to your Parabola Flow, make an API call to the file queue endpoint. The Pull from file queue step, once added and enabled, will show a modal containing the endpoint details. For example:

Any valid POST requests to that endpoint will trigger the Flow to run, processing the file using the file parsing settings within the step. Additional requests will be queued up to run one after another.

Alternatively, use the Run another Parabola Flow step with the following configuration to trigger runs of another Flow through the file queue:

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The Pull from Front step pulls in data from your Front account so you can quickly make insightful reports on your team and customers.

Connect your Front account

To connect your Front account, select  Authorize.

A new page will pop up asking you to Authorize again. After you do, it will return to your Parabola page.

Custom settings

Once you're back in the step's settings, in the Info Type dropdown menu, select the type of data you'd like to pull in

Here are the Info Types that are available:

• Events
• Teams
• Teammates
• Contacts
• Tags

Helpful tips

• Metrics on specific team members to entire team groups: In this step's settings Info Type > Teammates, select from an available team, and updated results will show one row of data per teammate with information like ID and email address. If you're looking to pull in metrics per teammate, then switch Metric Type > Table Metrics and explore ones like "team_table". There are also a few team-wide group metrics in Metric Type > Rollup Metrics like "avg_first_response_time".

• Time-based metrics show thousands of numbers in a column's row: these are in seconds. You can connect an Insert math column step to this Front import step to convert metric data from seconds into hours, days, or whichever time unit you'd like.

Use the Pull from Fulfil beta integration to bring key Fulfil data into Parabola — allowing you to transform your Fulfil data for more granular visibility, blend Fulfil data with information from other systems, and trigger alerts based on custom logic.

Fulfil is a beta integration which requires a more involved setup process than our native integrations (like Shopify and Google Analytics). Following the guidance in this doc should help even those without technical experience pull data from Fulfil. If you run into any questions, shoot our team an email at support@parabola.io.

🤝  Fulfil | Integration configuration

📖  Fulfil API reference:

https://docs.fulfil.io/developers

🔐  Fulfil Authentication reference:

https://docs.fulfil.io/developers/rest-api/authentication/personal-access-tokens

Instructions

1. Navigate to the main page of your ERP by swapping your {subdomain} in the URL: https://{subdomain}.fulfil.app/client/#/

2. Click on your username on the top right and then preferences

3. Select Manage personal access tokens.

4. In the upper right-hand corner select click the Generate Personal access token button.

5. Enter a helpful token description and click the Generate button.

6. Copy the API Key and store it somewhere safe.

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🔐  Parabola | Authentication configuration

1. Add a Pull from API or Enrich with API step to your canvas.

2. Click into the step to configure your authentication.

3. Under the Authentication Type, select None.

4. Click into the Request Settings.

5. Under Request Headers click + Add Header.

6. Enter a custom header of X-API-KEY in the left input box.

7. Paste in your API Key in the right input box.

Example Screenshot

🌐  Fulfil | Sample API Requests

Real-time inventory insights

Get started with this template

Retrieve Inventory Stock Locations

1. Click into the Pull from API: Retrieve Locations step.

2. Click into the Request Settings.

3. Under Request Headers click + Add Header.

4. Enter a custom header of  in the left input box.

5. Paste in your API Key in the right input box.

6. Navigate to the top of the Request Settings and swap in your Fulfil {subdomain} to duplicate the request settings below

API Endpoint URL

Request Body

7. Click Refresh results to display the results.

Example Screenshot

List inventory lines and quantities across stock locations

1. To use the same API credentials, click into the Enrich with API: List Inventory by Location step.

2. Click into the Request Settings.

3. Under Request Headers click + Add Header.

4. Enter a custom header of  in the left input box.

5. Paste in your API Key in the right input box.

6. Navigate to the top of the Request Settings and swap in your Fulfil {subdomain} to duplicate the request settings below:

API Endpoint URL

Request Body

7. Click Refresh results to display the results.

Example Screenshots

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Pagination for Advanced Filter Endpoints

You can set up pagination by selecting the Offset and Limit style pagination in Parabola. In our request body, we’ll use <%limit%> and <%offset%> as the values of the “offset” and “limit”. Once you do so, you’ll see that you can select these variables in the Pagination section below and set the actual pagination values for the request dynamically. The default offset is 0 and max limit is 500, so we’ll use those values in this step as well.

Purchase order (PO) monitoring

Get started with this template

List purchase orders

1. Click into the Pull from API: List Purchase Orders step.

2. Click into the Request Settings.

3. Under Request Headers click + Add Header.

4. Enter a custom header of X-API-KEY in the left input box.

5. Paste in your API Key in the right input box.

6. Navigate to the top of the Request Settings and swap in your Fulfil {subdomain} to duplicate the ****request settings below

API Endpoint URL

Request Body

Example Screenshot

Get purchase order lines and invoice data

1. To use the same API credentials, click into the Enrich with API: List Inventory by Location step.

2. Click into the Request Settings.

3. Under Request Headers click + Add Header.

4. Enter a custom header of X-API-KEY in the left input box.

5. Paste in your API Key in the right input box.

6. Navigate to the top of the Request Settings and swap in your Fulfil {subdomain} to duplicate the request settings below

API Endpoint URL

Request Body

7. Click Refresh data to display the results.

8. Use the Edit columns step to target specific columns from the API response and rename them.

9. Finish off the Flow by exporting your data using a Send to Parabola Table step.

Example Screenshot

Pulling data from Fulfil

These methods use the Advanced search & filter endpoint when pulling data from Fulfil, which you can learn more about here. With this endpoint, you must make a PUT request and specify which specific fields you’d like to be returned from the API.

Pagination for Advanced Filter Endpoints

You can set up pagination by selecting the Offset and Limit style pagination in Parabola. In our request body, we’ll use <%limit%> and <%offset%> as the values of the “offset” and “limit”. Once you do so, you’ll see that you can select these variables in the Pagination section below and set the actual pagination values for the request dynamically. The default offset is 0 and max limit is 500, so we’ll use those values in this step as well.

Use the Send to Geckoboard step to send your data to Geckoboard's data visualization tool and automatically update the underlying data of your dashboards.

Connect your Geckboard account

To connect your Geckoboard account, click Authorize.

‍Follow the link to lookup the Geckoboard API Key, copy from your Geckoboard account settings, and paste into Parabola. Click Authorize to complete the connection.

Custom settings

First, choose a Dataset Name. This name will auto-format to remove capital letters and spaces, as required by Geckoboard.

Using the dropdowns, map your data's columns to the appropriate field data types available in Geckoboard. If you want to make a line chart with this dataset, you must have a "Date" column.

Helpful tips

• If you are using this data to add a new widget in Geckoboard, select "Datasets" and then choose the corresponding dataset when you are prompted to choose a source in Geckoboard.
• You can only send a maximum of 5,000 rows to Geckoboard at a time. If you find yourself exceeding this limit, we suggest using the Remove rows step or grouping your data to reduce the number of data points.
• If you are sending date data, it must be in YYYY-MM-DD format, which you can easily achieve using the Format dates step.
• If you do not plan on sending every column of data to Geckoboard sure to use the Remove columns step prior to the Send to Geckoboard step to reduce the columns down to just the data you wish to send.

This step will only work with Google Analytics V4. If you have not yet migrated over to GA V4 and are using Google’s Universal Analytics, you will need to use the Pull from Google Analytics UA step to pull in your Google Analytics data. Google is deprecating Universal Analytics on July 1, 2023. Once you have moved your data over to Google Analytics 4, you will need to update your Flows to use this Parabola step to continue accessing your Google Analytics data. Read more about how Google is updating this here.

Use the Pull from Google Analytics 4 step to bring all of your Google Analytics data into Parabola in a familiar format. Choose a date range and which metrics and dimensions to pull in to create a report just like you are used to doing in Google Analytics.

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Connect your Google Analytics account

Begin by authenticating your Google Analytics account by clicking Authorize.

Choosing your settings

First, select the Account and Property that you would like to pull data from.

Then, select which metrics to pull in. These are same metrics that are available in Google analytics. Every metric that you add will result in a column being added to your report. You can select as many metrics as necessary for your report, including New Users, Bounces, Sessions, and many more.

Use dimensions to group your metrics and break them into more rows. Each dimension adds a column to the front of your table, and often will change any how many rows your report contains. Leaving the dimensions field blank will result in a single row of data.

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The time frame can be updated to let you pull data from:

1. In the last 7 days
2. The previous day
3. In the month to date
4. The previous X days/weeks/months/etc.
5. The current X day/week/month/year to date.
6. After a set date
7. Between two dates

You can also adjust for when you'd like the timeframe calculation to run, giving you the ability to pick between when the Flow is run or the most recently completed month/week/day/hour. The latter option is great for running a report for the last month, on the 1st of the following month, while excluding any data collected for far that day.

Lastly, if you choose, you can add offset for your date timeframe.

If you are looking to compare this data set to the same set, but from the previous period, a great way to do that is to pull in the two data sets, and the use the Combine tables step to combine them, using their dimensions in the matching rules.

Helpful tips

• This step is available starting at our Plus plan.
• The Pull from Google Analytics 4 step will only show you options for metrics and dimensions that are compatible (as defined by Google) with your current selection. You can explore valid combinations using this tool.

Migrating to the Google Analytics 4 step

Google is deprecating Universal Analytics on July 1, 2023. To continue accessing your Google Analytics data, you will need to update to Google Analytics 4 as outlined by Google here. Once you’ve migrated over to GA4, you will need to use this new Pull from Google Analytics 4 step to pull in your data.

In any existing Flow that has a Pull from Google Analytics step, you will need to replace it with a new Pull from Google Analytics 4 step.

Setting your replacement steps up should be as easy as replicating the metrics and dimensions that you were pulling.

Keep in mind that combinations of metrics and dimensions that may have been valid in Google Analytics UA (the prior version) may no longer be valid in Google Analytics 4. Our new Pull from Google Analytics 4 step will only show you options for metrics and dimensions that are compatible (as defined by Google) with your current selection.

Google is deprecating Universal Analytics on July 1, 2023. To continue accessing your Google Analytics data, you will need to update to Google Analytics V4 as outlined by Google here. Once you’ve migrated over to GA4, you will need to use our new "Pull from Google Analytics 4" step to pull in your data.

New data will continue to be pulled in by your existing Pull from Google Analytics steps until July 1, 2023. After that date, existing data will continue to be accessible in Parabola for at least 6 months, until Google no longer allows access to that historic data.

• New data will continue to be pulled in by your existing Pull from Google Analytics steps until July 1, 2023.
• After that date, existing data will continue to be accessible in Parabola for at least 6 months, until Google no longer allows access to that historic data.

If you have any questions, please reach out to help@parabola.io.

Connect your Google Analytics account

Begin by authenticating your Google Analytics account by clicking Authorize.

Default settings

Make sure that you're pulling data from the correct property and site.

You can adjust which property or site's data is being pulled into Parabola by selecting from the dropdown.

By default, the Pull from Google Analytics step will bring in Users by Medium.

The timeframe is also set to within the previous 1 week based upon when the flow is run, without an offset of dates.

Presets

We offer a variety of preset reports that are the same as those in the Google Analytics sidebar. Selecting a preset report will update the columns in your Metrics to use and Dimensions to use to group the metrics selection fields.

Use these as-is, or as a base for building your own customized reports.

Custom settings

You will find the Metrics to use field shows the same metrics you'd see in Google Analytics. Every metric that you add will result in a column being added to your report. You can select as many metrics as necessary for your report, including New Users, Bounces, Sessions, and more.

You can use various Dimensions to use to group the metrics, including Medium, Source, Campaign, Social Media, and more. Each dimension also adds a column, usually to the front, and it also will change how many rows you see in your data. Leaving this field blank will result in a single row of data, which is not grouped by anything.

The time frame can be updated to let you pull data from:

1. Between two dates

2. Between a date and today

3. The previous X days/weeks/months/etc.

4. The current X day/week/month/year to date.

You can also adjust for when you'd like the timeframe calculation to run, giving you the ability to pick between when the flow is run or the most recently completed month/week/day/hour. The latter option is great for running a report for the last month, on the 1st of the following month, while excluding any data collected for far that day.

Lastly, if you choose, you can add offset for your date timeframe.

If you are looking to compare this data set to the same set, but from the previous period, a great way to do that is to pull in the two data sets, and the use the Combine tables step to combine them, using their dimensions in the matching rules.

Helpful tips

• This step is available starting at our Plus plan.
• Please note that Google Analytics Reporting API V4 (App + Web) is not yet supported in this step. Use the Pull from Google Analytics 4 step to pull in data from your GA V4 account.

The Pull from Google Drive step gives you the ability to pull in CSV, Excel files, and Google Sheets from your Google Drive.

Connect your Google Drive account

To connect your Google Drive account, click Authorize to login with your Google account credentials.

Setting up the step

Use the file selector to select which file to pull data from

If you have multiple dataset sheets (tabs) in a file, specify which one you'd like to pull in by clicking on the dropdown menu under the file name.

You can also select to skip rows or columns of your choosing. This will skip rows from top-down and columns from left-to-right.

Helpful tips

• This step pulls data in exactly as it is shown in Google Drive. Formatted dates and numbers will be pulled in and shown in their formatted state. Visual formatting, such as colors, font changes, or sizing will not show up in Parabola.
• Any changes you make to the Google Drive file will be automatically updated when you run a flow, or manually updated if you click the refresh icon to the right of the step's name.
• This step can access any file in any Drive that your Google account has authorization to access.
• Google Sheets is unable to parse names that contain colons. Replace colons in the name of the sheet with an underscore or period to remove any colons.
• Other teammates who have access to a Flow where you have authenticated your Google account will not be able to see any other files in your Google Drive. If they want to update the file that the step is using, they will need to authenticate and select a file from their own Google Drive.

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The Send to Google Sheets step gives you the ability to automate sending custom datasets to Google Sheets. You can create a new Google Sheets file or update a specific sheet by having the dataset overwrite or append (add on) to an existing file.

Connect your Google Sheets account

To connect to your Google account, click Authorize to login with your Google account credentials.

Setting up the step

Select how you want this step to export data:

• Add data to an existing file (by overwriting the file or appending data to the bottom)
• Create a new file to write data to
• Create a new file on every run

Once you’ve selected a file to add data to, or have created a new file, select a sheet to send data to (one for each input to the Send to Google Sheets step).

When creating a new file or creating a new file on every run, you can select to create that file in the root of your Drive, or within a specific folder.

Helpful tips

• To ensure your header column titles carry over to your sheet, be sure to overwrite a sheet once. Any appending afterwards will still keep the header column titles. However, if you append to a blank sheet, header column titles won't be included in the exported data.
• This export step can accept multiple input sources, but here are a couple useful things to know. If you want your input sources to be exported to the same sheet, use the Stack tables step to combine your data set before sending it to the Send to Google Sheets step. If you want your input sources to be exported to different sheets, you'll have the option to select the specific sheet that your inputs should send data to.
• Anyone with access to the Google Sheet file will be able to see and use the updated datasets.
• This step can access any file in any drive that your Google account has authorization to access.
• Google Sheets has a 10 million cell limit. Any data being sent through a Send to Google Sheets step will need to be within that cell limit.
• Other teammates who have access to a Flow where you have authenticated your Google account will not be able to see any other files in your Google Drive. If they want to update the file that the step is using, they will need to authenticate and select a file from their own Google Drive.

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The Send to Google Drive step gives you the ability to export data to in CSV, Excel files, or Google Sheets in your Google Drive.

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Connect your Google account

To connect to your Google account, click Authorize to login with your Google account credentials.

Setting up the step

Select how you want this step to export data:

• Overwrite or append to an existing file
• Create a new file to write data to
• Create a new file on every run

Google Sheets files are the only file type that can have data appended.

For Excel and Google Sheets files, each input to the step can be used to populate data in a different tab of the file. CSV file may only accept a single input.

When creating a new file or creating a new file on every run, you can select to create that file in the root of your Drive, or within a specific folder.

Helpful tips

• To ensure your header column titles carry over to your sheet, be sure to overwrite a sheet once. Any appending afterwards will still keep the header column titles. However, if you append to a blank sheet, header column titles won't be included in the exported data.
• This export step can accept multiple input sources, but here are a couple useful things to know. If you want your input sources to be exported to the same sheet, use the Stack tables step to combine your data set before sending it to the Send to Google Sheets step. If you want your input sources to be exported to different sheets, you'll have the option to select the specific sheet that your inputs should send data to.
• Anyone with access to the Google Sheet file will be able to see and use the updated datasets.
• This step can access any file in any drive that your Google account has authorization to access.
• Google Sheets has a 10 million cell limit. Any data being sent through a Send to Google Sheets step will need to be within that cell limit.
• Other teammates who have access to a Flow where you have authenticated your Google account will not be able to see any other files in your Google Drive. If they want to update the file that the step is using, they will need to authenticate and select a file from their own Google Drive.

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Continually improve your customer experience by creating custom reports and processes based on your Gorgias tickets.

Gorgias is a beta integration which requires a slightly more involved setup process than our native integrations (like Facebook Ads and Google Analytics). Following the guidance in this doc should help even those without technical experience pull data from Gorgias. If you run into any questions, shoot our team an email at support@parabola.io.

Connect your Gorgias Account

To pull data from Gorgias, you'll need to start by accessing your Gorgias API Key. Here's a step-by-step:

1. Login into your Gorgias account and navigate to Settings ->REST API
2. Copy your Password (API Key)
3. In the "Pull from Gorgias" Parabola step, open the "Authentication" section and enter your Email and Password (API Key)  
4. Finally, change the "API Endpoint URL", replacing your-domain with the domain associated with your account

Helpful Information

• Other data: If you're hoping to pull data from Gorgias beside tickets, feel free to reference their API documentation
• If you're trying to pull in additional records, visit the "Rate Limiting" section and increase the "Max pages to fetch"

Use the Pull from HubSpot step to pull in Contacts, Companies, Deals, and Engagements data from your HubSpot CRM.

Connect your HubSpot account

To connect your HubSpot account, click Authorize.

Custom settings

Once you've logged in and authorized your HubSpot account, you can begin to pull in data from your Contacts, Companies, Deals, and Engagements records in your CRM by selecting a Data Type.

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When selecting a Data Type, you'll see an additional Properties dropdown. Here, you can add or remove columns from your data set.

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With the Contacts, Companies, and Deals datasets, you can also include historical data for all properties. This setting is not available for Engagements.

Helpful tips

• This step will only pull in the default fields. If you need a more custom pull, try using the Pull from an API step and connecting directly to HubSpot's API.

Use the Send to HubSpot step to send Contacts, Companies, and Deals data to your HubSpot CRM.

Connect your HubSpot account

To connect your HubSpot account, click Authorize.

Custom settings

Select the Data Type you're looking to update in HubSpot.

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All Data Types must include a column that maps to an ID. For Contacts, you may use the "Email" column as a unique identifier. For Companies, only a "companyId" property will suffice.

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Similarly, for Deals, a "deal ID" will be required to correctly map your data to HubSpot's data.

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Additionally, in order to send your data successfully to HubSpot, you will need to map every column of your dataset to a property that exists in HubSpot. If there are columns you do not want to be sent to HubSpot, try using our Select columns step to remove them prior to connecting to this Send to HubSpot step.

All other properties not mapped to your data's columns are optional.

Helpful tips

• If the Send to HubSpot step fails, it will not provide you with an error. Please contact us if you are experiencing errors.
• If you need additional customization, try using the Send to an API step and connecting directly to HubSpot's API.

The Use JSON file step enables you to pull in datasets from JSON files.

Custom Settings

To get started, either drag a file into the outlined box or click on Click to upload a file.

After you upload the file, the step will automatically redirect you to the settings page with a display of your JSON blob data.

In the Advanced Settings, you can set a number of rows and a number of columns to skip when importing your data. This will skip rows from top-down and columns from left-to-right.

Helpful Tips

Security

The files you upload through this step are stored by Parabola. We store the data as a convenience, so that the next time you open the flow, the data is still loaded into it. Your data is stored securely in an Amazon S3 Bucket, and all connections are established over SSL and encrypted.

Limitations

Parabola can't pull in updates to this file from your computer automatically, so you must manually upload the file's updates if you change the original file. Formatting and formulas from a file will not be preserved. When you upload this file, all formulas are converted to their value and formatting is stripped.

Stay up-to-date on your marketing KPIs by pulling metrics from Klaviyo's API. When set up correctly, this data will match what you see in your Klaviyo Dashboard.

Klaviyo is a beta integration which requires a slightly more involved setup process than our native integrations (like Facebook Ads and Google Analytics). Following the guidance in this doc should help even those without technical experience pull data from Klaviyo. If you run into any questions, shoot our team an email at support@parabola.io.

Connect your Klaviyo account

Get started by fetching your API key from Klaviyo. From the Klaviyo dashboard, click the icon in the top right and navigate to "Account" --> "Settings" --> "API Keys" --> "Create Private API Key". Once you generate an API Key, copy it and head back over to Parabola.

After dragging in our "Pull from Klaviyo" step, open up the step and paste your API key into the empty box under URL Parameters, to the right of "api_key."

Set your timezone

Regardless of the timezone that your Klaviyo account is set to, when you pull in data from Klaviyo's API, the timestamp is in UTC time. That means that if you don't adjust the timestamp, your metrics will not match what you see in Klaviyo.

• Search for a city in your account's timezone on this site: https://www.timeanddate.com/time/zone
• Find the filed that reads: Current Offset: UTC/GMT -X hours
• Next, convert X from hours to seconds by multiplying by 3600
• Input your calculated value in the "Set Timezone" step. If your offset is positive, change the minus sign to a plus sign.

For example, if my Klaviyo account is in PST, I would search for San Francisco to find that the time offset is -7 hours:

From there, I would multiple -7 by 3600 to get -25200. This value then goes in the "SET TIMEZONE" step, making that formula: {timestamp}-25200

If your offset is positive, the formula would be: {timestamp}+X

Set your date range

By default, this flow will summarize metrics from the previous complete day. This time frame is set in the "SET DATE RANGE" filter step. In the step before (the "Compare Dates" step), we are finding the number of days since an event occurred (ex. if something happened yesterday, the value would be between -1 and -2).

Toggle the filter settings to pull in your specified date range.

Helpful Tips

• Specific metric reporting: Want to pull in a specific metric instead of a full event timeline? Check out this video overview and Klaviyo's API docs
• Data not matching Klaviyo: Notice that your metrics are incorrect/ too low? If metrics are too low, in the "Pull Metrics" step, go to "Rate Limit" --> "Max pages to fetch" and increase the limit until you're pulling in all of your metrics. If you're still experiencing issues, make sure your timezone is correctly set following the guidance above.

Use the Pull from Looker step to run Looks and pull in that data from Looker.

Connect your Looker account

To connect to Looker, you’ll need to enter your Looker Client ID and your Looker API Host URL before authenticating:

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Finding your Client ID and Looker API Host URL

These steps only need to be followed once per Looker instance! If someone else on your team has done this, you can use the same Client ID that they have set up.

Your Looker permissions in Parabola will match the permissions of your connected Looker account. So you will only be able to view Looks that your connected Looker account can access.

1. Create a new user in Looker dedicated to authenticating with Parabola. You can skip this step if you are going to use an existing user.
2. The user will need to have User or Admin permissions in Looker in order to be able to find Looks and run them.
3. Click on the Edit button next to the user entry and click on Edit Keys next to the API3 Keys header to generate credentials.
4. Copy the Client ID, and go to the API Explorer in the Applications section of your Looker sidebar.
5. In the API Explorer, search for the Register OAuth App API call, click on it, and then click Run it.
6. In the API call run section, paste your Client ID in the first field, then set "redirect_uri" to the Parabola Redirect URI from this screen and “enabled” to true. It should look like this:
   ‍{
   "redirect_uri": "https://parabola.io/api/auth/looker/callback", "display_name": "Parabola OAuth Connection",
   "description": "",
   "enabled": true,
   "group_id": ""
   }
7. Run the call, and it should return a 200 OK response.
8. Paste your Client ID into the modal in Parabola.
9. Find and paste your Looker API Host URL into Parabola. This is usually the base URL that you see when accessing Looker, such as: https://company.cloud.looker.com
10. Click Submit and you will see a modal that will ask you to login to your Looker account and authenticate the connection the Parabola.

Custom settings

Once your step is set up, you can choose the Look that you want to run from the Run this Look dropdown:

There are also Cache settings that you can adjust:

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1. Ignore cache (default) - Ignores the cache that Looker has and asks for new data every time.
2. Use cache if available - Looker checks if the cache is recent enough and runs the Look if the data seems stale, otherwise it returns data from the Looker cache.
3. Only pull from cache - Looker only gives data back from their cache even if the data is out of date.

There are also additional settings that you can adjust within the step:

Perform table calculations: Some columns in Looker are generated from user-entered Excel-like formulas. Those calculations are not run by default in the API, but are run by default within Looker. This setting tells Looker to run those calculations.

Apply visualization options: Enable if you want things like the column names to match the names given in the Look, as opposed to the actual names of the columns in the source data.

Apply model-specific formatting: Requests the data in a way that respects any formatting rules applied to the data model. This can be things like date and time formats.

Common issues and how to troubleshoot

You may sometimes see a 404 error from the Pull from Looker step. Some common reasons for that error are:

1. The Look may not exist in the Production environment and needs to be pushed to production.
2. The authenticated user may not have the right permissions to run the Look and needs to get access in Looker.
3. The Look may have been deleted.

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The Pull from MS SQL step connects to and pulls data from a remote Microsoft SQL server. MS SQL is a relational database management system developed by Microsoft.

Connect your MySQL server

Double-click on the Pull from MS SQL step and click Authorize. These are the following fields required to connect:

• Hostname
• Username
• Password
• Database
• Port (Optional)

You should be able to find these fields by viewing your MS SQL profile.

If no port is specified during authorization, this step will default to port 1433.

You can leave fields blank (like "Password") if they are not needed for the database to authorize connection.

Custom settings

Once you are successfully connected to your server, you'll first see a dropdown option to select a table from your server. By default, Parabola pulls the whole table using the query: select *.

If you'd like to be able to pull in more specific, relevant data, or reduce the size of your default import, you can do so by writing your own SQL statement to filter your table's data.

To do so, click into the step's Advanced Settings and input your query into the Query (optional) field.

The Send to MS SQL step can insert and update rows in a remote Microsoft SQL server. MS SQL is a relational database management system developed by Microsoft.

Connect your MS SQL server

Double-click on the Pull from MS SQL step and click Authorize. These are the following fields required to connect:

• Hostname
• Username
• Password
• Database
• Port (Optional)

You should be able to find these fields by viewing your MS SQL profile.\

If no port is specified during authorization, this step will default to port 1433.

You can leave fields blank (like "Password") if they are not needed for the database to authorize connection.

Custom settings

Once you are successfully connected to your server, you'll first see a dropdown option to select the table you'd like to send data to.

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Maximum Connections

By default, this field is set to 20, which should be safe for most databases. This setting controls how many connections Parabola generates to your database in order to write your changes faster. The number of rows you are trying to export will be divided across the connections pool, allowing for concurrent and fast updates.

Be aware, every database has its own maximum number of connections that it will accept. It is not advisable to set the Maximum Connections field in Parabola to the same number of connections that your database max is set at. If you do that, you will be using every connection when the flow runs, and nothing else will be able to connect to your database. 50% - 60% of the total available connections is as high as you should go. Talk to the whoever runs, manages, or set up your database to find out how many connections it can handle.

If you set this field to less than 1 or more than the total number allowed by your database, the step will error.

‍

Operations

Next, you'll select an Operation. The available Operation options are:

• Insert: Inserts new rows in the database
• Upsert: Updates rows if possible, and inserts new rows if not
• Update: Only updates rows

Insert

The Insert option will insert new rows in the database. Once you select the "Insert" operation, you'll be asked to map your columns in Parabola to columns from your selected MS SQL table. You can leave some column mappings blank. If you're using the Insert operation, make sure that it's okay that Parabola create these new rows in your table. For example, you may want to check for possible duplicates.

Upsert

The Upsert option will updates rows if possible, and inserts new rows if not. The Upsert operation requires you to specify the primary key of the database table ("Unique Identifier Column in Database"), or the column that contains unique values in Parabola ("Unique Identifier Column in Results"). Mapping these columns is important so Parabola can use to figure out which rows to update versus insert new rows. A primary key / unique identifier must be configured on the database table in order for this dropdown to show any options.

Then, you need to map your columns in Parabola to columns from your selected MS SQL table.

Update

The Update option will only update rows. It will not insert any new rows. The Update operation requires you to specify the primary key of the database table ("Unique Identifier Column in Database"), or the column that contains unique values in Parabola ("Unique Identifier Column in Results"). Mapping these columns is important so Parabola can use to figure out which rows to update. A primary key / unique identifier must be configured on the database table in order for this dropdown to show any options.

Then, you need to map your columns in Parabola to columns from your selected MS SQL table.

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How this step deals with errors

The Send to MS SQL step handles errors in a different way than other steps. When the flow runs, the step attempts to export each row, starting at the top of the table and processing down until every row has been attempted. Most exports in Parabola will halt all execution if a row fails, but this export will not halt. In the event that a row fails to export, this step will log the error, but will skip past the row and continue to attempt all rows. When the step finishes attempting every row, it will either be in a normal (successful) state if every row succeeded, or it will be in a error (failure) state if at least 1 row was skipped due to errors.

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Helpful tips

• The Send to MS SQL step can only send strings. All values will be sent as a string. If you encounter an error when running the Send to MS SQL step, please double-check that the field in MS SQL is set to accept string values.
• Null (blank) values will create empty strings - meaning this step will not be able to send null values to your MS SQL database
• The names of your columns in Parabola must match the names of the fields in your database. Use a Rename columns step to accomplish this before sending.
• We recommend having all of your columns mapped. Any unmapped columns may cause issues during your export. If you need to remove any unmapped columns, you can utilize the Select Columns step.
• If the step is configured to map a particular column in Parabola to a column in your database, and during the export that column cannot be found or has been renamed, or cannot accept the value being sent, the step will not fail, but will instead skip trying to send that value for that row, and send a modified version of the row.
  

The Pull from Magento step pulls in data from your Magento instance. Magento is a flexible and powerful eCommerce solution that enables anyone to build a fully custom eCommerce solution. Magento can scale up or down to fit the exact needs of any eCommerce retailer.

Connect your Magento account

To connect your Magento account, you need to first create a SOAP/XML Role and then a SOAP/XML User.

Create a SOAP/XML Role

1. In the Magento admin panel choose System > Web Services > SOAP/XML - RPC Roles
2. Choose Add New Role
3. In the Role Info tab create a Role Name and choose Save Role
4. In the Role Resources tab change the Resource Access drop down option to ALL and then choose Save Role

Create a SOAP/XML User

1. In the Magento admin panel choose System > Web Services > SOAP/XML - RPC Users
2. Choose Add New User
3. Fill out the Account Information form generated. Both the User Name and the API Key are created by you.
4. Then choose Save User.
5. In the User Role tab allocate the User to the Role (you created earlier) and then choose Save User

Setting up the step in Parabola

In the Pull from Magento step, click the blue button to "Authorize". Provide the following: 1. Host 2. Port 3. API Path 4. API Username 5. API Key

Once you've populated the form, click on the blue button to "Authorize" to complete your connection.

Other tips on connecting Magento and Parabola

• Host should not include http:// or https://
• Ports are typically 80 for non-ssl, and 443 for ssl
• Paths should be the path to the xmlrpc api and should include the beginning /

Custom settings

The Pull from Magento step can pull can bring in Sales Orders, Customers, and Products. Select the appropriate dataset from the Dataset dropdown and select the date range we should use from the Created dropdown. Click Show Updated Results to see your data from Magento.

Use the Pull from Mailchimp step to retrieve data from your Mailchimp account. You can use Parabola to pull in List and Campaign data.

Connect your Mailchimp account

To connect your Mailchimp account, click Authorize to login with your Mailchimp credentials.

Custom settings

You can retrieve two different Data Type options from Mailchimp: List and Campaign.

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Once you select a data type, you'll be prompted to select your dataset, which can be either a List or a Campaign.

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A List pull will provide details in columns like "Email Address", "First Name", "Last Name", and so on from your Mailchimp Audience.

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A Campaign pull will provide detailed results of your email campaigns, such as the action taken and timestamp.

The Pull from OneDrive step gives you the ability to pull in datasets from your Microsoft OneDrive files.

Connect your OneDrive account

To connect your OneDrive account, click Authorize to login with your Microsoft account credentials.

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Selecting a file

To select the specific file you want to work with:

1. Select the drive - the dropdown will show all drives that you have access to
2. Search for the specific file you’re looking for - you can enter a term or paste in the entire file name. The dropdown will show all matching files
3. The first matching file will automatically appear in the Results view. You can select a specific sheet, for files with multiple

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File types supported

• XLS, XLSX*
• CSV (comma (,) delimiter)
• TSV (tab delimiter)
• JSON
• XML

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Helpful tips

• This step pulls data in exactly as it is shown in OneDrive. Formatted dates and numbers will be pulled in and shown in their formatted state. Visual formatting, such as colors, font changes, or sizing will not show up in Parabola.
• Any changes you make to the OneDrive file will be automatically updated when you run a Flow, or manually updated if you click the refresh icon to the right of the step's name.
• *The Microsoft API has a size limit for Excel workbooks of 100 MB. Attempting to pull an Excel workbook greater than 100 MB will result in an error, and you will be prompted to reduce the size of your workbook. This does not apply to .xls file extensions, only .xlsx.

The Send to Microsoft OneDrive step gives you the ability to automate sending custom datasets to OneDrive. You can create a new file or update a file by having the dataset overwrite or append (add on) to an existing file.

Connect your OneDrive account

To connect your OneDrive account, click Authorize to login with your Microsoft account credentials.

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Custom settings

First, select whether to create a new file, or update an existing file.

Create a new file:

Select the file type, and enter a file name. Then, indicate which drive the file should be saved to. Within a drive, you can either save to the root of the drive (default), or search for a specific folder to save to.

Update an existing file:

First, choose the file you want to update by selecting a drive. Then, search for the file by name.

Once your file is selected, you can decide how to update it:

1. Replace entire file - all file types
2. Append to bottom - Excel files only

(Note, you can specify which sheet of an Excel file to update.)

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File types supported

• XLSX
• CSV (comma (,) delimiter)
• TSV (tab delimiter)
• JSON
• XML

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Helpful tips

• Updates to Excel workbooks can take up to several minutes to be reflected in OneDrive.
• The Microsoft API has a size limit for Excel workbooks of 100 MB or 1,048,576 rows by 16,384 columns. (Read about other limits here.)
• If the size of the workbook you want to update already exceeds 100 MB or the max row and column limit, the API will return an error when Parabola attempts to fetch workbook metadata.
• If you are overwriting a worksheet and the data you're sending exceeds 100 MB or the max row and column limit, the Flow calculation will return an error. If you are appending to a worksheet and the data you're sending exceeds the remaining space of your workbook or will result in max row and column limit being exceeded, the Flow calculation will also return an error. In both cases you will be prompted to reduce the size of either your workbook or input data.

The Pull from SharePoint step gives you the ability to pull in datasets from your Microsoft SharePoint files.

Connect your SharePoint account

To connect your SharePoint account, click Authorize to login with your Microsoft account credentials.

Note: you may be asked to set up an authenticator app (for multi-factor authentication), or submit an authorization request to your IT administrator. This is dictated by your company’s Microsoft account settings.

‍

Selecting a file

To select the specific file you want to work with:

1. Select the site - the dropdown will show all SharePoint sites that you have access to
2. Select the drive
3. Search for the specific file you’re looking for - you can enter a term or paste in the entire file name. The dropdown will show all matching files
4. The first matching file will automatically appear in the Results view. You can select a specific sheet, for files with multiple

‍

File types supported

• XLS, XLSX*
• CSV (comma (,) delimiter)
• TSV (tab delimiter)
• JSON
• XML

‍

Helpful tips

• This step pulls data in exactly as it is shown in SharePoint. Formatted dates and numbers will be pulled in and shown in their formatted state. Visual formatting, such as colors, font changes, or sizing will not show up in Parabola.
• Changes you make to the SharePoint file can take several minutes to appear in Parabola (between 10-30 minutes).
• There may be some delay between updating your SharePoint site permissions and seeing those sites available in your Parabola step.
• Updates to Excel workbooks can take up to several minutes to be reflected in SharePoint.
• *The Microsoft API has a size limit for Excel workbooks of 100 MB. Attempting to pull an Excel workbook greater than 100 MB will result in an error, and you will be prompted to reduce the size of your workbook. This does not apply to .xls file extensions, only .xlsx.

Microsoft Azure User Permission Settings

Parabola is a verified app publisher, meaning we've been verified as authentic by Microsoft. Parabola will only have access to the items in SharePoint/OneDrive that the account used to authorize Parabola has access to (that's on a per-user basis).

In order to enable a user to import data from Microsoft and/or send it to Microsoft, Parabola requests the following permissions:

• Permission to read, create, update and delete OneDrive files the signed in user has access to
• Permission to read, create, update and delete documents and list items in all SharePoint sites the signed in user has access to
• Permission to read profile of signed-in users (e.g., name, email)
• Note, the "write" permissions are for our Send to SharePoint/OneDrive steps, so that you can create or update a file.

If you see "Need admin approval" when authorizing Parabola, this means that your Microsoft Azure user consent settings are configured to require admin approval for approving any app. An IT admin on your team can check these settings in Microsoft Azure > Enterprise Applications > Consent and Permissions. If you toggle to Azure's recommended setting of only allowing members of your Microsoft org to consent apps from verified publishers, you should no longer require admin approval.

The Send to Microsoft SharePoint step gives you the ability to automate sending custom datasets to SharePoint drives. You can create a new file or update a file by having the dataset overwrite or append (add on) to an existing file.

Connect your SharePoint account

To connect your SharePoint account, click Authorize to login with your Microsoft account credentials.

‍

Custom settings

First, select whether to create a new file, or update an existing file.

Create a new file

Select the file type, and enter a file name. Then, indicate where the file should be saved: select a site, and drive. Within a drive, you can either save to the root of the drive (default), or search for a specific folder to save to.

Update an existing file

First, choose the file you want to update by selecting a site, and drive. Then, search for the file by name.

Once your file is selected, you can decide how to update it:

1. Replace entire file - all file types
2. Append to bottom - Excel files only

(Note, you can specify which sheet of an Excel file to update.)

File types supported

• XLSX
• CSV (comma (,) delimiter)
• TSV (tab delimiter)
• JSON
• XML

‍

Helpful tips

• Updates to Excel workbooks can take up to several minutes to be reflected in SharePoint.
• The Microsoft API has a size limit for Excel workbooks of 100 MB or 1,048,576 rows by 16,384 columns. (Read about other limits here.)
• If the size of the workbook you want to update already exceeds 100 MB or the max row and column limit, the API will return an error when Parabola attempts to fetch workbook metadata.
• If you are overwriting a worksheet and the data you're sending exceeds 100 MB or the max row and column limit, the Flow calculation will return an error. If you are appending to a worksheet and the data you're sending exceeds the remaining space of your workbook or will result in max row and column limit being exceeded, the Flow calculation will also return an error. In both cases you will be prompted to reduce the size of either your workbook or input data.

The Pull from MongoDB step enables you to connect to your MongoDB database and access your NoSQL data in Parabola. MongoDB is a document-oriented database platform, also classified as a NoSQL database program.

Connect to your MongoDB account

Double-click on the Pull from MongoDB step and click on the blue button to Authorize. These are the following fields required to connect:

• Hostname
• Username
• Password
• Database
• Port
• Full URI (Optional) - You can opt to paste in the full URI instead of filling out the fields above.

Default settings

Once you are successfully connected to MongoDB, your first collection will be pulled in automatically. You can update the imported collection by clicking on the Collection dropdown.

Custom settings

Select your desired collection from the Collection dropdown menu.

Helpful tips

• Once your JSON blobs from MongoDB are pulled into Parabola, use the Expand JSON step or the Split column step to further query inside your JSON blobs.

The Pull from MySQL step connects to and pulls data from a remote MySQL server. MySQL is an open-source relational database management system developed by Oracle.

Connect your MySQL server

Double-click on the Pull from MySQL step and click Authorize. These are the following fields required to connect:

• Hostname
• Username
• Password
• Database
• Port (Optional)

You should be able to find these fields by viewing your MySQL profile.

If no port is specified during authorization, this step will default to port 3306.

You can leave fields blank (like "Password") if they are not needed for the database to authorize connection.

Custom settings

Once you are successfully connected to your server, you'll first see a dropdown option to select a table from your server. By default, Parabola pulls the whole table using the query: select *.

If you'd like to be able to pull in more specific, relevant data, or reduce the size of your default import, you can do so by writing your own SQL statement to filter your table's data.

To do so, click into the step's Advanced Settings and input your query into the Query (optional) field.

The Send to MySQL step can insert and update rows in a remote MySQL database. MySQL is an open-source relational database management system developed by Oracle.

Connect your MySQL server

Double-click on the Send to MySQL step and click on the blue button to Authorize. These are the following fields required to connect. You should be able to find these fields by viewing your MySQL profile.

If no port is specified during authorization, this step will default to port 3306.

You can leave fields blank (like password) if they are not needed for the database to authorize connection.

Custom settings

Once you are successfully connected to your server, you'll first see a dropdown option to select the table you'd like to send data to.

‍

Maximum Connections

By default, this field is set to 20, which should be safe for most databases. This setting controls how many connections Parabola generates to your database in order to write your changes faster. The number of rows you are trying to export will be divided across the connections pool, allowing for concurrent and fast updates.

Be aware, every database has its own maximum number of connections that it will accept. It is not advisable to set the Maximum Connections field in Parabola to the same number of connections that your database max is set at. If you do that, you will be using every connection when the flow runs, and nothing else will be able to connect to your database. 50% - 60% of the total available connections is as high as you should go. Talk to the whoever runs, manages, or set up your database to find out how many connections it can handle.

If you set this field to less than 1 or more than the total number allowed by your database, the step will error.

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Operations

Next, you'll select an Operation. The available Operation options are:

• Insert: Inserts new rows in the database
• Upsert: Updates rows if possible, and inserts new rows if not
• Update: Only updates rows

Insert

The Insert option will insert new rows in the database. Once you select the "Insert" operation, you'll be asked to map your columns in Parabola to columns from your selected MySQL table. You can leave some column mappings blank. If you're using the Insert operation, make sure that it's okay that Parabola create these new rows in your table. For example, you may want to check for possible duplicates.

Upsert

The Upsert option will updates rows if possible, and inserts new rows if not. The Upsert operation requires you to specify the primary key of the database table ("Unique Identifier Column in Database"), or the column that contains unique values in Parabola ("Unique Identifier Column in Results"). Mapping these columns is important so Parabola can use to figure out which rows to update versus insert new rows. A primary key / unique identifier must be configured on the database table in order for this dropdown to show any options.

Then, you need to map your columns in Parabola to columns from your selected MySQL table.

Update

The Update option will only update rows. It will not insert any new rows. The Update operation requires you to specify the primary key of the database table ("Unique Identifier Column in Database"), or the column that contains unique values in Parabola ("Unique Identifier Column in Results"). Mapping these columns is important so Parabola can use to figure out which rows to update. A primary key / unique identifier must be configured on the database table in order for this dropdown to show any options.

Then, you need to map your columns in Parabola to columns from your selected MySQL table.

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How this step deals with errors

The Send to MySQL step handles errors in a different way than other steps. When the flow runs, the step attempts to export each row, starting at the top of the table and processing down until every row has been attempted. Most exports in Parabola will halt all execution if a row fails, but this export will not halt. In the event that a row fails to export, this step will log the error, but will skip past the row and continue to attempt all rows. When the step finishes attempting every row, it will either be in a normal (successful) state if every row succeeded, or it will be in a error (failure) state if at least 1 row was skipped due to errors.

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Helpful tips

• The Send to MySQL step can only send strings. All values will be sent as a string. If you encounter an error when running the Send to MySQL step, please double-check that the field in MySQL is set to accept string values.
• Null (blank) values will create empty strings - meaning this step will not be able to send null values to your MySQL database
• The names of your columns in Parabola must match the names of the fields in your database. Use a Rename columns step to accomplish this before sending.
• We recommend having all of your columns mapped. Any unmapped columns may cause issues during your export. If you need to remove any unmapped columns, you can utilize the Select Columns step.
• If the step is configured to map a particular column in Parabola to a column in your database, and during the export that column cannot be found or has been renamed, or cannot accept the value being sent, the step will not fail, but will instead skip trying to send that value for that row, and send a modified version of the row.

The Pull from NetSuite integration enables users to connect to any NetSuite account and pull in saved search results that have been built in the NetSuite UI. Multiple saved searches, across varying search types, can be configured in a single flow.

The following document outlines the configuration requirements in NetSuite for creating the integration credentials, defining relevant role permissions, and running the integration in Parabola.

NetSuite configuration process

The following configuration steps are required in NetSuite prior to leveraging the Parabola integration:

• Create or select a web services only role that can be used by Parabola
• Create or select a user that will be used for the integration in NetSuite. Ensure the role from the step above is applied to this user record
• Create a new integration in Netsuite
• This will result in the creation of your consumer key and consumer secret
• Create a new set of access tokens that reference the user, role, and integration specified above
• This will result in the creation of your token id and token secret

Once complete, you will enter the unique credentials generated in the steps above into the Pull from NetSuite step in Parabola. This will also require your account id, which is obtained from your NetSuite account’s url. Ex: https://ACCOUNTID.app.netsuite.com/

The following document will review how to create each of the items above.

Creating a NetSuite role

The permissions specified on the role applied to your integration will determine which saved searches, transactions, lists, and results you’ll be able to access in Parabola. It is important for you to confirm that the role you plan to use has access to all of the relevant objects as required.

The following permissions are recommended, in addition to any specific transaction/list/report specific you may require.‍

In addition to the below permissions, we also recommend adding the permissions listed in this document.

Transactions

• Any specific transaction types required: sales orders, purchase orders, transfer orders, etc.
• Find transaction

Reports

• Any specific report types required

Lists

• Any specific lists required: items, locations, companies, customers, etc.
• Perform search, persist search, and publish search

Setup

• Log in using Access Tokens
• SOAP Web Services

Custom Records:

• Any specific custom record objects required

Ensure the checkbox for the web services only role is selected.

Creating a NetSuite integration

Video walk-though of the setup process:

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Follow the path below in the NetSuite UI to create a new integration record.

1. Setup > Integration > Manage Integrations > New
2. Specify an integration name, ensure the status is set to active, and select the token-based authentication option.
3. Uncheck the TBA: Authorization Role and Authorization Code Grant checkboxes.
4. Save the record.

A consumer key and consumer secret will be generated upon saving the record. Record these items, as they will disappear once you leave this page.

Creating a new access token

Once the role, user, and integration have been created, you’ll need to generate the tokens which are required for authentication in Parabola.

Follow the path below in the NetSuite UI to create a new token record.

1. Setup > Users/Roles > Access Tokens > New Access Tokens
2. Specify the integration created previously, the desired user, and role, and click save.
3. The newly created token id and token secret will appear at the bottom of the page. Record these credentials, as they will disappear once you leave this page.

Configure your settings in Parabola

1. Gather the credentials created from each step earlier in the process and navigate to the Pull from NetSuite step in Parabola.
2. Open the Pull from NetSuite step and click Authorize or Edit Accounts
3. Enter each applicable token and consumer key/secret and click authorize.

Once authorized, you’ll be prompted to select a search type and specific saved search to run. Click refresh and observe your results!

The Return only columns specified in the search checkbox enables a user to determine if all available columns, or only the columns included in the original search, should be returned. This setting is helpful if you’d like to return additional data elements for filtered records without having to update your search in NetSuite.

Helpful Tips

• The Pull from NetSuite step integrates directly with the saved search function. Based on permissions, users have the ability to access all saved searches from the NetSuite UI within Parabola.
• If no saved search options are returned for a specific transaction type, please validate your user and role have access to the specific object you’re attempting to access.
• Users will need permissions within NetSuite to create new integrations, manage access tokens, edit roles, etc. in order to generate the credentials required for this integration
• Formula fields within saved searches will not be returned
• Saved searches which include summary results are not supported
• Ensure the user/role configured for the integration has sufficient permissions to access all necessary saved searches and results

By default, the NetSuite API will only return the full data results from the underlying search record type (item, customer, transaction, etc) and only the internal ids of related record types (vendors, locations, etc) in a search.

For example, running the following search in Parabola would return all of the information as expected from the base record type (item in this scenario), and the internal id of the related object (vendor).

The best way to return additional details from related objects (vendor in this scenario) is by adding joined fields within the search. Multiple joined fields can be added to a single search to return data as necessary.

Alternatively, another solution would be running separate searches and joining the results by using a Combine Tables step within the flow. This is demonstrated below.

Usage notes

• Users will need permissions within NetSuite to create new integrations, manage access tokens, edit roles, etc. in order to generate the credentials required for this integration
• Formula fields within saved searches will not be returned
• Saved searches which include summary results are not supported
• Ensure the user/role configured for the integration has sufficient permissions to access all necessary saved searches and results

The NetSuite REST Web Services API is used to interact programmatically with NetSuite data, allowing developers to manage, retrieve, and manipulate data and execute business operations directly in NetSuite. SuiteQL is a query language to provide advanced query capabilities to access your NetSuite records and data.

Use Cases

🤝  NetSuite | Integration configuration

📖  NetSuite API Reference docs:

https://docs.oracle.com/en/cloud/saas/netsuite/ns-online-help/chapter_1540811107.html

🔎  NetSuite SuiteQL Example docs:

https://docs.oracle.com/en/cloud/saas/netsuite/ns-online-help/section_156257790831.html#subsect_156474852230

🔐  NetSuite Authentication docs:

https://docs.oracle.com/en/cloud/saas/netsuite/ns-online-help/section_158074210415.html

Instructions

1. Ensure appropriate features are enabled
   1. Navigate to Setup > Company > Enable features > SuiteCloud tab.
      1. Enable the following features:
         1. Select SuiteTalk > Rest Web Services.
         2. Select Manage Authentication > OAuth 2.0.
2. Ensure the role assigned to the user that will be leveraged for the integration has the following permissions:
   1. Enable permissions for your role: Setup > Users/Roles > Manage roles > Name of your role > Edit.
   2. Enable the following permissions:
      1. Select Permissions > Setup > Log in using OAuth 2.0 Access Tokens(Full), OAuth 2.0 Authorize Applications Management(Full) and REST Web Services(Full).
3. Create OAuth 2.0 Client for NetSuite
   1. Navigate to Setup > Integrations > Manage Integrations > New
   2. Give it a name Parabola OAuth 2.0
   3. Uncheck all items under token-based authentication
   4. Under OAuth2.0
      1. Check Authorization Code Grant
      2. Check Rest Web service' under Scopes
      3. Add https://parabola.io/api/steps/generic_api/callback within the Redirect URI field
      4. Click save
   5. Copy the following values that are generated:
      1. Application ID
      2. Client ID
      3. Client Secret

🔐  Parabola | Authentication configuration

1. Add a Send to API step to your canvas.
2. Click into the step to configure your authentication.
3. Under the Authentication Type, select OAuth 2.0 before selecting Configure Auth.
4. Toggle on Switch to custom settings

Auth name

Give your authorization account an identifiable name.

Authorization Request URL (GET)

https://<account-id>.app.netsuite.com/app/login/oauth2/authorize.nl

💡 Tip: Swap in your account-id into the Authorization Request URL.

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‍URL Parameters

Access Token Request URL (POST)

https://<account-id>.suitetalk.api.netsuite.com/services/rest/auth/oauth2/v1/token

💡 Tip: Swap in your account-id into the Authorization Request URL.

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Body Parameters

Custom Headers

💡 Tip: You can base64 encode your <client ID>:<client Secret> using a base64 encoding tool like this one

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Refresh Token Request URL (POST)

https://<account-id>.suitetalk.api.netsuite.com/services/rest/auth/oauth2/v1/token

💡 Tip: Swap in your account-id into the Authorization Request URL.

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Body Parameters

Custom Headers

💡 Tip: You can base64 encode your <client ID>:<client Secret> using a base64 encoding tool like this one

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Example Screenshot

🌐  NetSuite | Sample API Requests

Query NetSuite data with SuiteQL

Run a SuiteQL query to retrieve data from a  record. Get started with this template.

1. Add a Query NetSuite Data with SuiteQL step to your Flow.
2. Click into the Pull from API: SuiteQL step.
3. Under Authentication Type, select OAuth 2.0 to use your authentication credentials.
4. Click into the Request Settings to configure your request using the format below:

API Endpoint URL

Request Body

Pagination Style

1. Select Offset and Limit
2. Set the Offset key field .
3. Set the Starting value field to 0.
4. Set the Increment by field to 100.
5. Set the Limit key field to limit.
6. Set the Limit value field to 100.
7. Enter the number of pages to return using Pages to fetch while editing and Pages to fetch while running fields. Each page returns 100 orders.
8. Click Refresh data to display the results.
9. Use an Expand JSON step to parse nested JSON objects into a tabular structure.
10. Finish off Flow by exporting you date using a Send to Parabola Table step.

Example Screenshots

Update Sales Order Line Items

Update the properties of your sales orders by making API requests to NetSuite’s SuiteTalk REST web services. Get started with this template

1. Use a Pull from NetSuite step to pull in a saved Transaction search with applicable order details.
2. Connect it to a Send to API: Update NetSuite SO Line Items step to your Flow.
3. Under Authentication Type, select OAuth 2.0 to use your authentication credentials.
4. Click into the Request Settings to configure your request using the format below:

API Endpoint URL

⚠️ Note: An Internal Id associated with the sales order must be provided.

Request Body

⚠️ Note: Depending on the property, custom fields can accept string values or Internal Ids. If a property is configured to accept an Id, using the property’s literal string value will throw an error.

Example Screenshot

Instantly leverage AI-generated text in your Parabola Flows using our GPT-3 Beta integration. Build prompts to generate custom messaging, analyze content in emails and attachments, generate new product listings – the possibilities are endless.

If you're new to APIs, this integration guide should help you set up the API connection in minutes. If you run into any issues, reach out at support@parabola.io.

Connect your OpenAI Account

To get started, you'll need to add your OpenAI API key to the Parabola "Prompt GPT-3" step. To find your API key, follow these steps:

1. Go to the OpenAI website and sign in to your account
2. Navigate to the API Keys section in the dashboard (or click this link)
3. Generate a new API key if you haven't already, or use an existing key
4. Copy the key and paste it in the "Authentication" section of the "Prompt ChatGPT" step

Please note that, to use the GPT-3 API, you need to have an OpenAI API key and also check their pricing plan or pricing models.

Helpful Information

• Utilize the API's advanced options for fine-tuning the generated text
• Experiment with different input fields to see how it affects the output
• Utilize the output in other steps in your flow for further processing
• The API imposes a max content length that is quite short on their free tier, so expect shorter results during initial testing than you would get from the full tool

Example Use Cases

Here are 10 ChatGPT-generated use case examples: 

1. Personalizing customer communications: Generate personalized emails, text messages, and social media posts to increase engagement and conversion rates; push results into your CRM for sequencing
2. Summarizing information: Summarize large amounts of text data, such as product descriptions or emails, making it easier to quickly understand and act on important information
3. Generating product descriptions: Generate unique and compelling product descriptions, helping businesses stand out in a crowded marketplace. Take it a step further by asking for feedback on your top-performing products.
4. Writing social media posts: Generate social media posts that are tailored to specific audiences and platforms, increasing engagement and reach
5. Automating customer support: Generate responses to common customer support inquiries, freeing up time and resources for businesses
6. Improving SEO: Generate unique and SEO-friendly content for websites and blogs, helping businesses rank higher in search results
7. Creating chatbot scripts: Generate scripts for chatbots, allowing businesses to offer 24/7 customer support and improve the overall customer experience
8. Generating landing pages: Generate custom landing pages for different campaigns and audiences, increasing conversion rates
9. Creating email campaigns: Generate effective and targeted email campaigns, increasing open and click-through rates
10. Enhancing data analysis: Generate insights and recommendations from large sets of data, helping make more informed decisions

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You can import PDF files in a few different ways:

• Upload a file directly using the Extract from PDF file step
• Pull PDFs from inbound email using the Extract from email step
• Bulk process PDF files using the Pull from file queue step

Check out this Parabola University video for a quick intro to our PDF parsing capabilities, and see below for an overview of how to read and configure your PDF data in Parabola.

Understanding your PDF data

Parabola’s Pull from PDF file step can be configured to return Columns or Keys

• Columns are parts of tables that are likely to have more than one row associated with them
• Keys are single pieces of data that are applicable to the entire document. As an example - “Total” rows or fields like dates that only appear once at the top of a document are best expressed as keys
• Sometimes AI can interpret something as a column or a key that a human might consider the other. If the tool is not correctly pulling a piece of information, you might try experimenting with columns versus keys for that data point
• Both columns and keys can be given additional information from you to ensure the tool is identifying and returning the correct information - more on that below!

Step Configuration

You can use Extract from PDF, Extract from email, and Pull from file queue to parse PDFs. Once you have a PDF file uploaded into your Flow, the configuration settings are uniform.

Extract a table

1. Auto-detected Table (default)
Parabola scans your PDF, detects possible tables, and labels the most likely columns. This option uses LLM technology and works exceptionally well if the PDF document has a clear, structured table. All detected tables will be available in the sub-dropdown under the "Use an auto-detected table" dropdown.

• Quickest setup
• Works best when your table has headers
• You can manually add more columns or keys after

2. Define a Custom Table
‍Manually define the structure of your table if the AI didn’t pick it up. You can name the table and define the columns that you want to extract from the PDF by clicking on the + Add Column button.

• Good for multi-table documents
• Works well with tables spread across multiple pages
• Requires a bit more setup

3. Extract All Data (OCR-first mode)
Use OCR to return all text from the PDF — helpful if the structure is complex or you're feeding the result into an AI step later. We only recommend this option if the first two extraction methods aren't yielding the desired results.

Return formats:

• All data → Every value, one per row
• Table data → Tables split by page, each with a table ID
• Key-value pairs → Labeled items like SKU: 12345
• Raw text → One cell per page, useful for follow-up AI parsing

Extract values

If there are document-level values like invoice date and PO number that you want to extract, add them as keys in this section. You can add this by clicking on the “+ Add key” button. Each key that you configure will be represented as its own column and the value will be repeated across all the rows of the resulting data set.

• Column and key names can be descriptive or instructive, and do not need to match exactly what the PDF says. However, you should try to ensure the name is something that the underlying AI can associate with the desired column of data
• Providing examples is the best way to increase the accuracy of column (or key) parsing
• The “Additional instructions to find this value” field is not required, however, here you can input further instructions on how to identify a value as well as instructions on how to manipulate that value. For example in a scenario where you want to make two distinct columns out of a singular value in the file, say an order number in the format “ABC:123".  You might use the prompt - “Take the order ID and extract all of the characters before the “:” into a new column”

See below how in this case with handwriting, with more instructions the tool is able to determine if there is writing next to the word “YES” or “NO”.

Fine Tuning

You can give the AI more context by typing additional context and instructions into this text box. Try using specific examples, or explain the situation and the specific desired outcome. Consult the chat interface on the lefthand side to help you write clear instructions.

Advanced Settings

1. Text parsing approach
You can specify the text parsing approach if necessary. The default setting is “Auto” and we recommend keeping it this way if possible. If it’s not properly parsing your PDF, you can choose between “OCR” and “Markdown”.

• OCR - This will use a more sophisticated version of OCR text extraction that can be helpful for complex documents such as those with handwriting. This more advanced model may, however, result in the tool running slower.
• Markdown - This will use Markdown for parsing. It is generally faster for parsing and may work better for certain documents, like pdfs that have nested columns and rows.

2. Retry step on error
The checkbox will be checked by default. LLMs can occasionally return unexpected errors and oftentimes, re-running the step will resolve the issue. When checked, this step will automatically attempt to re-run one time when encountering an unexpected error.

3. Auto-update prompt versions
The checkbox will be unchecked by default. Occasionally Parabola updates step prompts in order to make parsing results more accurate/reliable. These updates may change output results, and as a result, auto-updating is turned off by default. Enable this setting to always use the most reset prompt versions.‍

4. Page filtering
The checkbox will be unchecked by default. This setting allows users to define specific pages of a document to parse. If you only need specific values that are consistently on the same page(s), this can drastically improve run time. If you do check this box off, please make sure to complete the dropdown settings that appear below.

• Keep, Remove, or Autodetect
  
  • The Autodetect option will allow the parser to choose what pages to use.

• The first, the last, or these
  
  • If you select “the first”, input a number in the “#” box to instruct how many pages from the beginning of the file should be parsed.
    
  • If you select “the last”, input a number in the #” box to instruct how many pages from the end of the file should be parsed.
  • If you select “these”, input a comma-separated list of numbers in the blank box to specify which pages. For example, if you put “1, 10, 16”, the step will parse the first, tenth, and sixteenth page only of the file.

Usage tips & Other Notes

• The more document pages that are needed for parsing, the longer it may take. To expedite this process, you can configure the step to only review certain pages from your file. The fewer the pages, the faster the results!
• If you need to pull data across multiple tables (from a single file), you will likely need multiple steps – one per table.
• File size: PDF files must be <500 MB and 30 pages
• PDFs cannot be password protected
• We recommend always auditing the results returned in Parabola to ensure that they’re complete

Using child columns

Mark columns as “Child columns” if they contain rows that have values unique from the parent columns:

Before:

After marking “Size” as a child column:

Use Extract from PDF to work with a single PDF file. Upload a file by either dragging a PDF file anywhere onto the canvas, or click "Click to upload a file" to select a file from your file picker.

Step configuration instructions can be found here.

Extract from email can pull in data from a number of filetypes, including attached PDF files. Once configured, Parabola can be set to parse PDFs anytime the relevant email receives a PDF file.

Step configuration instructions can be found here.

Pull from file queue - PDF files

Pull from file queue can receive PDF files and parse the relevant data. The file queue is a way to enqueue a Flow to run with a series of metadata + a file that is accessible via URL.

Runs can be added to the file queue via API (webhook) or via Run another Parabola Flow.

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The Run another Parabola Flow step gives you the ability to trigger runs of other Parabola flows within a flow.

Running other Flows

Select the flow you want to trigger during your current flow's run. No data will pass through this step. It's strictly a trigger to automatically begin a consecutive run of a secondary flow.

However, if you choose “Run once per row with a file URL”, data will be passed to the second Flow, which can be read using the Pull from file queue step.

Use the Run behavior setting to indicate how the other Flow should run. The options that include wait will cause the step to wait until the second Flow has finished before it can complete it’s calculation. The other options will not wait.

Using this step in a Flow

This step can be used with or without input arrows. If you place this step into a Flow without input arrows, it will be the first step to run. If it does have input arrows, then it will run according to the normal sequence of the Flow. Any per row options require input arrows.

Helpful tips

• This step is only available on our Advanced plan.
• It can be beneficial or necessary to split large and complex Parabola flows into multiple pieces. In order for your data to processed correctly, you may need a flow to run exactly after another flow. In this case, you can add the Run another Parabola flow step destination after the last step of your flow, and have it trigger the next flow in your sequence.
• The flow that you're trying to trigger must be published. If you're unable to find a flow in the drop-down list, make sure it is published (ie. a live version of the flow exists).

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The Pull from Parabola Table step is a source step used to pull data from a Parabola Table that you have access to. If you are an Editor or Viewer on a Flow, any Parabola Tables on that Flow will be available to be pulled in as a data source using this step.

Settings

The dropdown options for Tables to import will be located on the left-hand side. Tables that you have access to will be listed in the dropdown options. This step can access any Table in your Parabola account that you are authorized to access (whether as Viewer, Editor or owner).

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Helpful Tips

This step pulls the base data in your Parabola Table. Views applied on to your table, such as filters, sorts, aggregations, groups and visual formatting will not show up in this step.

If you do not see your Parabola Table in the dropdown, check to make sure the Allow other Flows to pull data from this table option is enabled on your Send to Parabola Table step.

If you need to bring in multiple Tables, use multiple Pull from Parabola Table steps to bring in the data. Then combine the dataset using a Stack tables or Combine tables step.

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Limitations: when working across multiple Flows, the Pull from Tables step will only pull from a Table that has been published on a Flow with a successful run. When working within the same Flow, you can also pull from a draft (unpublished) table.

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The Send to Parabola Table step is a destination step that lets you store your dataset in a Parabola Table. Data sent to that table will be visible to anyone with access to that Flow (Viewer, Editor or Owner).

Viewing your data

When configured, the Send to Parabola Table step has two tabs - an "Input" tab, and an "Existing Table" tab.

• The input tab will show you what data is currently flowing into this step. That is the data that would be sent to the Table to be stored, if you ran this Flow.
• The existing Table tab will show you the data that is currently stored in the Table from prior runs of the Flow. Any downstream steps that receive input data from this step will have access to the data represented in this tab. Any steps that receive input from this step will receive the data currently stored in the Table.

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Settings

1. After you connect your Flow to this export step, it will show a preview of the Table data. You must give your Table a name on this step (and you can edit the name from here at any time).

1. By default, the Allow other Flows to pull data from this table option will be checked. When checked, teammates with any access to this Flow will be able to use this Table as source data in other Flows (using a Pull from Parabola Tables step). To prevent other Flows from pulling this Table, disable this option. Tables will still be visible in the Flow’s Live page, and can be pulled within the same Flow.
2. Choose how the data will be added to the Table. Data can be overwritten on each run, appended to the bottom or top (prepend), or updated based on certain values (upsert).

Overwrite the table

• With this option, each run of your Flow will completely replace the data in the Table
• Note: To de-dupe a table, you'll need to overwrite the whole table.

Append new data

• With this option, each run of your Flow will add new rows either below, or above, the existing rows in the Table. All rows that are sent to this step will be added to the table
• Choose to match the columns in the input data in the step to the table by matching the position or name of each column in the table
• Optional: you can include a timestamp that indicates when each row was added to the table

Update existing rows

• With this option, each run of your Flow will attempt to update matching rows within the table
• Rows will be matched based on the combination of values in all columns selected. If duplicate matches exist, the step will error when the Flow runs. All matching column name must be present in both the data in the step and the table, and must be exact matches by name
• If a match is not found for a particular row in the step, it can be appended to the bottom of the table or discarded
• Optional: you can include a timestamp that indicates when each row was last updated

Storing data

Once you run your Flow, the Table will populate and update.Tables are useful to store data that can be accessed between Flows or to create reports when used in conjunction with the Visualize step. More info on how to visualize here.

Running another Flow after updating a Table

Use an arrow to connect this step to other steps in a sequence. For example, you can connect this step to the Run another Flow step to first send data to a Table and then run a Flow that pulls data from that Table.

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Helpful Tips

• The data you send through this step is stored by Parabola. We store the data as a convenience, so that the next time you open the Flow, the data is loaded into it. Your data is stored securely in an Amazon S3 Bucket, and all connections are established over SSL and encrypted.
• Your Table’s content is never discarded. To remove the data, you will need to delete the step from both draft and live versions of the flow (or delete the entire Flow).
• Parabola Tables will be limited to our current cell count limitation (described here).
• To clear a Table, we recommend adding a Limit rows step before your Send to Parabola Table step.  Limit the dataset to 0 rows. Set the step to "Overwrite table". Run your Flow.

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Parashift is an Intelligent Document Processing platform that provides Out-of-the-box solutions for data extraction from various types of documents, including PDFs. Parashift leverages proprietary AI-based technology to read and parse documents, resulting in cleaned data that is available via API.

Parabola’s beta integration with Parashift receives parsed PDF data in real time via Webhook and makes that data accessible along with any other data source or target within Parabola.

The following document outlines how to configure the required Webhook settings in Parashift and integration in Parabola.

Generate Webhook Endpoint in Parabola

The first step in the configuration process is generating a webhook URL in Parabola that can be added in Parashift. Review our Receive from webhook page for detailed overview of how to create a webhook and retrieve the corresponding URL.

Enable Webhook in Parashift

Navigate to the Webhooks page, listed under the </> Development section, within the side panel in your Parashift account.

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Create a new webhook using the “+ New” icon in the top right of the screen. Give your newly created webhook a name and paste in the Parabola URL that was generated in the previous step.

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Enable the Processing Finished checkbox within the Deliver Topic. This will ensure a message is posted to the Parabola webhook each time a document is uploaded to Parashift and finishes processing. Additional topics can be selected if you’d like to receive other types of notifications within Parabola. Click save once complete.

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Receiving Parashift webhook data

Parashift will send a message to the specified Parabola webhook for each event type specified in the section above. These messages will typically include a batch ID, document ID, status, and timestamp. An example of the Processing Finished message is below:

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Generating an API Key

Navigate to the API Keys page, listed under the </> Development section, within the side panel in your Parashift account.

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Create a new API Key using the “+ New” icon in the top right of the screen. Give your newly created API Key a name and click save. Your API key will become visible and can be copied from this screen.

Once completed, this API key should be passed in all API requests to Parashift as a Bearer Token.

Retrieving Document Details from Parashift

After receiving a message that a document has finished processing, the next step is to retrieve the document details. An API call can be made to the following endpoint to return the parsed attributes of a given document.

https://api.parashift.io/v2/documents/{attributes document_id}/?include=document_fields&extra_fields[document_fields]=extraction_candidates

The API response will leverage the JSON:API specification, which will require expanding several JSON objects in Parabola in order to effectively work with the data. An example of this process is below and also included as part of the beta integration

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Resources

• Parashift Website
• Parashift API documentation
• Parashift Sales contact information: sales@parashift.io

Use the Pull from ParseHub step to pull in data from your webscraping data in ParseHub.

Connect your ParseHub account

To connect to your ParseHub account, select Authorize in the left-side toolbar.

You'll be prompted to insert your ParseHub API Key, which can be found on your account settings page.

Enter your API Key and select Authorize.

Custom settings

Select your Project from the dropdown in the settings bar.

Your data from the most recent web scrape will now be pulled into Parabola.

Use the Send to ParseHub step to send dynamic data to ParseHub to kick off a web scraping project.

Connect your ParseHub account

To connect to your ParseHub account, select Authorize.

You'll be prompted to insert your ParseHub API Key, which can be found on your account settings page.

Enter your API Key and select Authorize.

Custom settings

Choose the Project you'd like ParseHub to run from the dropdown in the settings bar.

Choose the columns that contains the values that your ParseHub project is expecting in its Start Values section.

If you already have the URLs to use defined in your ParseHub project, and would not like to send ParseHub any start URLs, then you can target a blank column to send.

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The Pull from PostgreSQL step connects to and pulls from a PostgreSQL database. PostgreSQL is an open-source object-relational database management system.

Connect your PostgreSQL database

Double-click on the Pull from PostgreSQL step and click Authorize. These are the following fields required to connect:

• Hostname
• Username
• Password
• Database
• Port (Optional)

If no port is specified during authorization, this step will default to port 5439.

You can leave fields blank (like password) if they are not needed for the database to authorize connection.

Custom settings

Once you are successfully connected to your PostgreSQL server, you'll first see a dropdown option to select a table from your server. By default, Parabola pulls the whole table using the query:

select *

If you'd like to be able to pull in more specific, relevant data by writing your own SQL statement, you can do so by clicking into "Advanced Settings" and input your query into the Query (optional) field.

The Send to PostgreSQL step can insert and update rows in a remote PostgreSQL server. PostgreSQL is an open source relational database management system.

Connect your PostgreSQL server

Double-click on the Pull from PostgreSQL step and click Authorize. These are the following fields required to connect:

• Hostname
• Username
• Password
• Database
• Port (Optional)

You should be able to find these fields by viewing your PostgreSQL profile.

If no port is specified during authorization, this step will default to port 5432.

You can leave fields blank (like "Password") if they are not needed for the database to authorize connection.

Custom settings

Once you are successfully connected to your server, you'll first see a dropdown option to select the table you'd like to send data to.

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Maximum Connections

By default, this field is set to 20, which should be safe for most databases. This setting controls how many connections Parabola generates to your database in order to write your changes faster. The number of rows you are trying to export will be divided across the connections pool, allowing for concurrent and fast updates.

Be aware, every database has its own maximum number of connections that it will accept. It is not advisable to set the Maximum Connections field in Parabola to the same number of connections that your database max is set at. If you do that, you will be using every connection when the flow runs, and nothing else will be able to connect to your database. 50% - 60% of the total available connections is as high as you should go. Talk to the whoever runs, manages, or set up your database to find out how many connections it can handle.

If you set this field to less than 1 or more than the total number allowed by your database, the step will error.

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Operations

Next, you'll select an Operation. The available Operation options are:

• Insert: Inserts new rows in the database
• Upsert: Updates rows if possible, and inserts new rows if not
• Update: Only updates rows

Insert

The Insert option will insert new rows in the database. Once you select the "Insert" operation, you'll be asked to map your columns in Parabola to columns from your selected PostgreSQL table. You can leave some column mappings blank. If you're using the Insert operation, make sure that it's okay that Parabola create these new rows in your table. For example, you may want to check for possible duplicates.

Upsert

The Upsert option will updates rows if possible, and inserts new rows if not. The Upsert operation requires you to specify the primary key of the database table ("Unique Identifier Column in Database"), or the column that contains unique values in Parabola ("Unique Identifier Column in Results"). Mapping these columns is important so Parabola can use to figure out which rows to update versus insert new rows. A primary key / unique identifier must be configured on the database table in order for this dropdown to show any options.

Then, you need to map your columns in Parabola to columns from your selected PostgreSQL table.

Update

The Update option will only update rows. It will not insert any new rows. The Update operation requires you to specify the primary key of the database table ("Unique Identifier Column in Database"), or the column that contains unique values in Parabola ("Unique Identifier Column in Results"). Mapping these columns is important so Parabola can use to figure out which rows to update. A primary key / unique identifier must be configured on the database table in order for this dropdown to show any options.

Then, you need to map your columns in Parabola to columns from your selected PostgreSQL table.

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How this step deals with errors

The Send to PostgreSQL step handles errors in a different way than other steps. When the flow runs, the step attempts to export each row, starting at the top of the table and processing down until every row has been attempted. Most exports in Parabola will halt all execution if a row fails, but this export will not halt. In the event that a row fails to export, this step will log the error, but will skip past the row and continue to attempt all rows. When the step finishes attempting every row, it will either be in a normal (successful) state if every row succeeded, or it will be in a error (failure) state if at least 1 row was skipped due to errors.

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Helpful tips

• The Send to PostgreSQL step can only send strings. All values will be sent as a string. If you encounter an error when running the Send to PostgreSQL step, please double-check that the field in PostgreSQL is set to accept string values.
• Null (blank) values will create empty strings - meaning this step will not be able to send null values to your PostgreSQL database
• The names of your columns in Parabola must match the names of the fields in your database. Use a Rename columns step to accomplish this before sending.
• We recommend having all of your columns mapped. Any unmapped columns may cause issues during your export. If you need to remove any unmapped columns, you can utilize the Select Columns step.

Pull data on all of your subscription customers using our Recharge beta step. Track how many new customers subscribed/ cancelled in a day, and report on order data passing through Recharge.

Recharge is a beta integration which requires a slightly more involved setup process than our native integrations (like Facebook Ads and Google Analytics). Following the guidance in this doc should help even those without technical experience pull data from Recharge. If you run into any questions, shoot our team an email at support@parabola.io.

Connect your Recharge account

Follow the guidance in this post from Recharge to secure your API key. In your "Pull from Recharge" step, this key will go in the "Request Header" section under "X-Recharge-Access-Token".

Specify your date range

To specify a date range in your 'pull orders' step, visit the 'Set timeframe' step and modify the start and end dates.

Helpful Information

• Access additional data: Trying to pull in a different set of data from Recharge? Check out their API reference.
• Questions? Shoot us an email at support@parabola.io

The Pull from Redshift step connects to and pulls data that is stored in your Amazon Redshift database. Amazon Redshift is a data warehouse product within the AWS ecosystem.

Connect your Amazon Redshift database

Double-click on the Pull from Redshift step and click Authorize. These are the following fields required to connect:

• Hostname
• Username
• Password
• Database
• Port (Optional)

If no port is specified during authorization, this step will default to post 5439.

You can leave fields blank (like Password) if they are not needed for the database to authorize connection.

Custom settings

Once you are successfully connected to your database, you'll first see a dropdown option to select a table from your Redshift database. By default, Parabola pulls the whole table using the query:

select *

If you'd like to be able to pull in more specific, relevant data, or reduce the size of your default import, you can do so by writing your own SQL statement to filter your table's data.

To do so, click into "Advanced Settings" and input your query into the Query (optional) field.

The Pull from Salesforce step gives you the ability to bring data from your Salesforce CRM into Parabola by object type and fields. You can also filter your results by a selected View which can be set within Salesforce.

Connect your Salesforce account

Connect your Salesforce account by clicking Authorize and following the prompts to log in with your Salesforce details.

Custom settings

When pulling in data from Salesforce, you can select the Object Type and View. Object Types are based upon the objects you would find in Salesforce, for example, Accounts, Opportunities, and Contacts.

Views give you the ability to trim down the results of your pull using Salesforce's native View criteria as a filter.

Your data will return in the same structure as your View within Salesforce. Under "Advanced Settings", you'll see the ability to choose the Fields that return with your data. This will override the default structure set from your selected View.

Helpful tips

• The Pull from Salesforce step will not connect to Salesforce Essential accounts.
• Reports are limited to 2000 rows by the Salesforce API. To overcome it, you need to pull in the underlying objects into Parabola with Pull from Salesforce step and recreate the logic of the view.

Use the Send to Salesforce step to add or update data from Parabola to your Salesforce CRM.

Connect your Salesforce account

Connect your Salesforce account by clicking Authorize and following the prompts to log in with your Salesforce details.

Default settings

The default Operation will be to Upsert, which will map your data to existing records and create new records if there is no match. You can also select Insert, which will only create new records, but this may create duplicate records.

Custom settings

Select the appropriate Object Type to ensure your records are correctly mapping to your CRM. These Object Types are similar to the Pull from Salesforce step, and include Account, Opportunities, Contact, amongst others.

All columns must be mapped to their corresponding Salesforce fields, and the Upsert operation requires a column to be apped to "Id". This is the Id of the object you are targeting, such as lead or contact. To map your columns, click the dropdown menus and select each matching Salesforce field. The names of your columns do not need to match the fields.

Helpful tips

• The Send to Salesforce step will not connect to Salesforce Essential accounts.
• Salesforce ignores empty values when updating records. To upsert (replace) existing values with blank values, replace your blank field values with #N/A. You can use the Find and replace step to accomplish this.
• The Salesforce API may send back a "success" response, even if there were errors in the request. If you're able to access SF Setup Page -> Bulk Data Load Jobs, you'll be able to see the data loads from the last 7 days. Find the Job related to the Parabola upload to view the result of the upload, along with error messaging to get a better understanding of any upload errors.

The Use sample data step allows you to quickly begin building a Flow leveraging sample datasets. This is particularly useful when you want to test Parabola’s data transformation and visualization features, but don’t necessarily want to integrate your live data sources yet.

This step provides both generic data, such as US census and stock market data, as well as data that resembles specific tools like Shopify, ShipHero, Salesforce, and NetSuite.

Using the step

Simply drag a Use sample data step from the Integrations tab of the search bar onto the canvas to immediately begin seeing data in Parabola. Double-click the step to view and modify the sample data that you’re working with.

Sample data options

This step includes both generic datasets as well as tool-specific datasets.

Beyond generic datasets like census and stock market data, the step also includes datasets that resemble what the data will actually look like when you pull it from another system.

For instance, if you select the “Shopify: Orders” sample data, the table returned will actually resemble the Pull from Shopify step’s output.

Next steps

Once you have your sample data loaded up, imagine what you might do if you were working with that data in a spreadsheet. Would you do any filtering? What math calculations might you apply? Do the dates need to be reformatted?

Once you know how you want the data to be transformed, then you can shift focus to what step you need to use to apply that transformation. Check out the Transformations section of the search bar (and search for keywords) to find the right step for the job.

The Send emails by row step sends one email per row in your dataset using the email address listed in a specific column. This is useful for sending personalized messages to a list of recipients. The step supports up to 75 emails per run and all messages are sent from team@parabolamail.io, with a footer that says "Powered by Parabola."

Setting Up the Step

1. Add the step to your Flow by dragging it onto the canvas.
2. Connect it to the last step that contains your column of email addresses.
3. Open the step to configure its settings.
4. Recipients: Choose the column with the email addresses.‍
5. Body Format: Choose between plain text and HTML.‍
6. Subject and Body: These are required fields. You can personalize them by merging values from other columns using {curly braces}.‍
7. Reply To: Enter the email address where replies should be sent.

Helpful tips

• Use HTML formatting in the Body field by selecting HTML as the format.
• Common HTML tags like <br>, <b>, and <a> are supported.
• Avoid exceeding the 75-recipient limit per run to prevent errors.
• If you need to send a single email with a file attached, use the “Email a file attachment” step instead. Unlike “Send emails by row,” which sends one email per row, the "Email a file attachment" step sends one email total with a file attachment—ideal for sharing reports or exports with a fixed list of recipients.

The Send to SendGrid step gives you the ability to automatically send emails through SendGrid without code. Quickly build and iterate sales, marketing, and internal solutions without tying up engineering resources.

Connect your SendGrid account

To begin, click Authorize and login with your credentials.

You will need your API Key to link your SendGrid account to Parabola. You can find that on your SendGrid account's Settings API Keys page.

The API Key will be obfuscated by a row of dots, but you can just select the dots and copy and paste into Parabola and your key will be added.

Custom settings

First, select your column of recipient email addresses. Each row will receive one email. If you have duplicate email addresses, they will receive multiple emails. Try using our Remove duplicate rows step to remove for duplicate addresses prior to connecting the data to the this step.

Enter the email address that you'd like  emails to be set from in the Send From field. The Send to SendGrid step can only send from a single address.

Next, enter your Sender Name and Email Subject.

Now you can select your Email Content Type. You can choose between Text and HTML. To use Text, your email will appear in plain text, which can be written out in the Email Body field directly. To use HTML, you can also enter your formatted HTML in the Email Body field.

Enter your text in the Email Body field. You can reference column data to use as a mail merge in both the Email Subject and Email Body by wrapping the column names in {curly braces}. If the body of your email is already in a column, simply reference that column with a merge value. Be aware that if your email body column itself includes merge fields, those fields will need to be merged prior to this step. All merges used in the Email Body and Email Subject fields will appear in the email as they do in the column.

Helpful tips

• Try using dummy data when building a flow using this destination. Use a recipient email address that you control so that you can send test emails and ensure that your merge values appear as intended and that the email looks free of errors in an email client like Gmail or Outlook.

Pull data from ShipHero to create custom reports, alerts, and processes to track key metrics and provide a great customer experience.

ShipHero is a beta integration which requires a more involved setup process than our native integrations (like Shopify and Google Analytics). Following the guidance in this doc (along with our video walkthrough) should help even those without technical experience pull data from ShipHero.

If you run into any questions, feel free to reach out to support@parabola.io.

Access the ShipHero integration

Inside your flow, search for "ShipHero" in the right sidebar. When you drag the step onto the canvas, a card containing 'snippets' will appear on the canvas. To start pulling in data from ShipHero, copy a snippet and paste it onto the canvas (how to paste a snippet).

Connect your ShipHero account

We must start by authorizing ShipHero's API. In the "Pull from ShipHero" step's Authentication section, select "Expiring Access Token". For the Access Token Request URL, you can paste: https://public-api.shiphero.com/auth/token

In the Request Body Parameters section, you can "+add" username and password then enter your ShipHero login credentials. A second Request Header called "Accept" will exists by default – this can be deleted. Once completed, step authorization window should look as so:

Custom Settings

When you drag the ShipHero step onto the canvas, there will be 5 pre-built snippets available:

• Shipments
• Orders
• Returns
• Purchase Orders
• Products

For everything besides Products, it's common to pull in data for a specific date range (ex. previous day or week). This is why the card begins with steps that specify a dynamic date range. For example, if you put -2 as the Start Date and -1 as the End Date, you will pull orders from the previous full day.

If you're wanting to pull data from ShipHero that is not captured by these pre-built connections, you can modify the GraphQL Query and/or add Mutations by referencing ShipHero's GraphQL Primer.

Troubleshooting missing data

By default, we pull in 20 pages of data (2,000 records). To increase this value, visit the "Pull from ShipHero" step and go to "Rate Limiting" --> "Maximum pages to fetch
" and increase the value until all of your data is pulled in.

Helpful Tips

• Calculation Errors: The more complex your query is, the more likely the request is to fail. If you're receiving a "Calculation error", this is likely because of the complexity of your query. These results can be unstable once you begin hitting that error. To reduce the complexity of your query, eliminate any columns that you don't need from your request body, and check out ShipHero's documentation.
• GraphQL: To learn more about making GraphQL API calls in Parabola, check out our API docs

The Pull from ShipStation step allows you to pull in orders, shipments, and fulfillments from your ShipsStation account.

Connect your ShipStation account

After clicking Authorize, you'll need to get your API Key and Secret and add them, which will enable this flow to pull from your ShipStation account. You can find your API Key and Secret here: https://ship11.shipstation.com/settings/api.

Default settings

By default, thstep will pull in Orders that were created within the last week. The orders pull defaults to also pulling in the line items for each order. This means that each row represents an item in an order. You can also pull in Shipments and Fulfillments.

Across Orders, Shipments, and Fulfillments, you can modify the time frame, default or all columns, and you can filter based on things like status and carrier.

Custom settings

Orders

When pulling in shipments, you can select to pull in the default column set or all columns. By default, the Orders pull includes line items. You can change this by updating the settings to show orders without line items

Orders can be filtered in this step to only include those with order status (i.e. Awaiting Shipment).

Orders can also be filtered down by the date they were created.

Shipments

When pulling in shipments, you can select to pull in the default column set or all columns. By default, the Shipments pull includes line items. You can change this by updating the settings to show shipments without line items. Shipments can be filtered in this step to only include those sent via a certain carrier (i.e. UPS). Shipments can also be filtered down by the date they were created.

Fulfillments

When pulling in fulfillments, you can select to pull in the default column set or all columns. Fulfillments can also be filtered down by the date they were created.

Helpful tips

• This step is available starting at our Plus plan.

The ShipStation API is used for managing and automating shipping tasks, integrating with e-commerce platforms, and streamlining order fulfillment and shipment processes.

ShipStation is a beta integration which requires a slightly more involved setup process than our native integrations. Following the guidance in this document should help even those without technical experience pull data from ShipStation. If you run into any questions, shoot our team an email at support@parabola.io.

Use Cases

🤝 ShipStation | Integration configuration

📖 ShipStation API reference docs:

https://www.shipstation.com/docs/api/

🔐 ShipStation Authentication docs:

https://www.shipstation.com/docs/api/requirements/#authentication

Instructions

1. Navigate to your ShipStation settings in your account.

2. In the API Keys section, create or regenerate your API Key and API Secret.

3. Save your credentials before connecting to Parabola.

🔐 Parabola | Authentication

1. Add a Pull carrier rates from ShipStation step template to your canvas.

2. Click into the Pull from API: Carriers step to configure your authentication.

3. Under the Authentication Type, select None.

4. Click into the Request Settings to configure your request using the format below:

Request Headers

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💡 Tip: You can configure an Authorization Header Value using a base-64 encoder. Encode your Client ID and Client Secret separated by a colon: Client ID:Client Secret.

In Parabola, use the Header Value field to type Basic , followed by a space, and paste in your encoded credentials: Basic {encoded credentials here}.

5. Click into the Enrich with API: ShipStation Rates step and apply the same authentication settings used in steps 1-4.

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Example Screenshot

⚠️ Note: In this example, the API Key is api_key. The API Secret is api_secret.

Base-64 encoding the API Key and API Secret, separated by a colon, generates the following string: YXBpX2tleTphcGlfc2VjcmV0

🌐 ShipStation | Sample API Requests

Calculate shipping rates by ShipStation carriers

Get started with this template.

Load sample orders

1. Add a Use Sample data step to your canvas. You also can import a dataset with tracking numbers into your Flow (Pull from Excel File, Pull from Google Drive, Pull from API, etc.)

2. Select the Ecommerce: Orders dataset and click Refresh Data.

💡 Tip: Connect the sample data to a Limit rows step to get rates for 1 sample order.

3. Use an Add text columns step to generate a new column Merge.

• Set the column value to 1.

List all Shipstation carriers

4. Add a Pull from API step beneath the Use sample data step.

5. Click into the step. Under Authentication Type, select None.

6. Click into the Request Settings and configure a request to list all carriers in your ShipStation account:

API Endpoint URL

Request Headers

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7. Click Refresh data to display the results.

8. Select orders as a Nested Key.

9. Click Refresh data once more to expand the order data into a table.

Example Screenshot

10. Connect this step to Edit columns step.

11. In the Edit columns step, keep the name and code columns.

12. Use an Add text columns step to generate a new column Merge.

• Set the column value to 1.

Map ShipStation carriers to sample orders

13. Use a Combine tables step and connect these steps:

• Input 1: Add text column (Load sample data)
• Input 2: Add text column (Import ShipStation carriers)

14. Click into the step to configure the settings.

• Keep all rows from Add text column (Load sample data)
• Keep only matching rows from Add text column (Import ShipStation carriers)
• Where the Merge column matches.

Merge product weight and dimensions

15. Copy and paste the Products - Weight and dimensions.csv file snippet into your flow: parabola:cb:86331de2-e00b-4634-b629-d37098bbbdfe

16. Use another Combine tables step and connect these steps:

• Input 1: Combine tables
• Input 2: Pull from CSV file

17. Click into the step to configure the settings.

• Input 1: Combine tables
• Input 2: Pull from CSV file
• Where the Product Title and Product columns match

Get carrier shipping rates for sample orders

18. Connect the dataset to an Enrich with API step.

19. Click into the step. Under Authentication Type, select None.

30. Click into the Request Settings to configure a request to get shipping rates for the specified shipping details:

API Endpoint URL

Request Body

Request Headers

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Example Screenshots

Template Screenshot

📣 Callouts

Note: The weight of the order must be provided in the API request. The dimensions are optional. Consider using an Add math column and Sum by group steps to calculate weight and dimension values by order and quantity.

The Pull from Shopify step can connect directly to your Shopify store and pull in orders, line item, customer, product data and much more!

This step can pull in the following information from Shopify:

• A list of Orders with the following details: Orders, a list of Line Items sold for each order, with refunds and fulfillment included, a list of Shipping Lines for each order, and Discount Applications that have been applied to your orders

• Your Shop Balance
• A list of your shop Customers
• A list of shop Disputes
• Your Product Inventory Levels per location
• A list of Location Details associated with your shop
• A list of shop Payouts
• A list of all Collections with their associated products
• A list of Products

Connect your Shopify account

Select the blue Authorize button. If you're coming to Parabola from the Shopify App Store, you should see an already-connected Pull from Shopify step on your flow.  

Default settings

By default, once you connect your Shopify account, we'll import your Orders data with Line Items detail for the last day. From here, you can customize the settings based on the data you'd like to access within Parabola.

Custom settings

This section will explain all the different ways you can customize the data being pulled in from Shopify. To customize these settings, start by clicking the dropdown in part 2 of the step.

Pulling your Orders

Shopify orders contain all of the information about each order that your shop has received. You can see totals associated with an order, as well as customer information and more. The default settings will pull in any Order with the Orders detail happened in the last day. This will include information like the order total, customer information, and even the inventory location the order is being shipped from.

If you need more granular information about what products were sold, fulfilled, or returned, view your Orders with Line Items detail. This can be useful if you want relevant product data associated with each line item in the order. 

Available filters for orders, line items, shipping lines, and discount applications

• Choose to include the default columns (most commonly used) or include all columns (every field that your orders contain).
• Choose to include or not include test orders
• Filter by order status: any, cancelled, closed, open, open and closed
• Filter by financial status: any, authorized, paid, partially_paid, partially_refunded, pending, refunded, unpaid, voided
• Filter by fulfillment status: any, shipped, partial, unshipped, unfulfilled (partial + unshipped)

Date filters for orders, line items, shipping lines, and discount applications

• Choose to filter your data by order processed date or refund processed date
• within the previous # day, hour, week, or month
• based on when the flow is run or the most recently completed day
• You can also add an offset to the previous period or previous year. We have a handy helper to confirm the date range we'll use to filter in the step:

• within the current day to date, week to date, month to date, year to date
• You can add an offset to the previous period or previous year.
• after x date
• between x and y dates

Pulling your Line Items, with refunds and fulfillments

Each order placed with your shop contains line items - products that were purchased. Each order could have many line items included in it. Each row of pulled data will represent a single item from an order, so you may see that orders span across many rows, since they may have many line items.

There are 4 types of columns that show up in this pull: "Orders", "Line Items", "Refunds", and "Fulfillment columns". When looking at a single line item (a single row), you can scroll left and right to see information about the line item, about its parent order, refund information if it was refunded, and fulfillment information if that line item was fulfilled.

Pulling your Shipping Lines

As your orders are fulfilled, shipments are created and sent out. Each shipment for an order is represented as a row in this pull. Because an order may be spread across a few shipments, each order may show up more than one time in this pull. There are columns referring to information about the order, and columns referring to information about the shipment that the row represents.

Pulling your Discounts

Every order the passes through your shop may have some discounts associated with it. A shopper may use a few discount codes on their order. Since each order can have any number discount codes applied to it, each row in this pull represents a discount applied to an order. Orders may not show up in this table if they have none, or they may show up a few times! There are columns referring to information about the order, and columns referring to information about the discount that was applied.

Pulling your Shop Balance

This is a simple option that pulls in 1 row, containing the balance of your shop, and the currency that it is set to.

Pulling your Customers

This option will pull in 1 row for every customer that you have in your Shopify store records.
Available filters:

• Choose to include the default columns (most commonly used) or include all columns (every field that your orders contain).
• By default, we will only pull in the default address for each customer. Because customers may have more than one address, you can select the checkbox to "Expand rows to include all addresses". If you select this option, any customer with more than a single address will show up on multiple rows. For example, if your customer Juanita has 3 addresses in your system, then you will see 3 rows for Juanita, with the address information being the only data that is different for each of her rows.

Date filters for customer data:

• Choose to filter your data by order processed date or refund processed date
• Within the previous # day, hour, week, or month
• Based on when the flow is run or the most recently completed day
• You can also add an offset to the previous period or previous year. We have a handy helper to confirm the date range we'll use to filter in the step:

• Within the current day to date, week to date, month to date, year to date
• You can add an offset to the previous period or previous year.
• After x date

Pulling your Disputes

Retrieve all disputes ordered by the date when it was initiated, with the most recent being first. Disputes occur when a buyer questions the legitimacy of a charge with their financial institution. Each row will represent 1 dispute.

Pulling your Product Inventory

An inventory level represents the available quantity of an inventory item at a specific location. Each inventory level belongs to one inventory item and has one location. For every location where an inventory item is available, there's an inventory level that represents the inventory item's quantity at that location.

This includes product inventory item information as well, such as the cost field.

You can choose any combination of locations to pull the inventory for, but you must choose at least one. Each row will contain a product that exists in a location, along with its quantity.

Toggle "with product information" to see relevant product data in the same view as the Product Inventory.

Pulling your Location Details

This is a simple option that will pull in all of your locations for this shop. The data is formatted as one row per location.

Pulling your Payouts

Payouts represent the movement of money between a Shopify Payments account balance and a connected bank account. You can use this pull option to pull a list of those payouts, with each row representing a single payout.

Pulling your Collections

Pull the name, details, and products associated with each of your collections. By default, each row returns the basic details of each collection. You can also pull the associated products with each collection. 

Available filters:

• You can pull in a list of your manual collections. A manual collection contains a list of products that are manually added to the collection. They may have no relation to each other.
• You can pull in a list of your smart collections. A smart collection contains a list of products that are automatically added to the collection based on a set of shared conditions like the product title or product tags.

Pulling your Products

This pulls in a list of your products. Each row represents product variant since a product can have any number of variants. You may see that a product is repeated across many rows, with one row for each of its variants. When you set up a product, it is created as a variant, so products cannot exist without having at least one variant, even if it is the only one.

Available filters:

• Choose to include the default columns (most commonly used) or include all columns (every field that your orders contain).
• By default, we will only pull in one image per variant. Because you may have multiple images per variant, you can select the checkbox to "Expand rows to include all images". If you select this option, for product variants with many images, each image will be added to a new row, so product variant XYZ may show up on 3 rows if there are 3 images pulled for it.
• You can also filter down your products by a few attributes: collection_id, handle, product_type, published status, title, and vendor.

The Send to Shopify step can connect directly to your Shopify store and automatically update information in your store.

This step can perform the following actions in Shopify:

• Create new customers
• Update existing Customers
• Delete existing Customers
• Add products to collections
• Delete product-collection relationships
• Update existing inventory items
• Adjust existing inventory levels
• Reset inventory levels
• Issue refunds by line items

Connect your Shopify account

To connect your Shopify account from within Parabola, click on the blue "Authorize" button. For more help on connecting your Shopify account, jump to the section: Authorizing the Shopify integration and managing multiple stores.

Custom settings

Once you connect a step into the Send to Shopify step, you'll be asked to choose an export option.

The first selection you'll make is whether this step is enabled and will export all data or disabled and will not export any data. By default, this step will be enabled, but you can always disable the export if you need to for whatever reason.

Then you can tell the step what to do by selecting an option from the menu dropdown.

Create New Customers

When using this option, every row in your input data will be used to create a new customer, so be sure that your data is filtered down to the point that every row represents a new customer to create.

When using this step, any field that is not mapped in the settings table in the step will not be sent. Only the mapped fields will be sent to Shopify.

Every customer must have either a unique Phone Number or Email set (or both), so be sure those fields are present, filled in, and have a mapping.

If you create customers with tags that do not already exist in your shop, the tags will still be added to the customer.

The address fields in this step will be set as the primary address for the customer.

Update Existing Customers

When using this option, every row in your input data will be used to update an existing customer, so be sure that your data is filtered down to the point that every row represents a customer to update.

When using this step, any field that is not mapped in the settings table in the step will not be sent. Only the mapped fields will be sent to Shopify.

Every customer must have a Shopify customer ID present in order to update successfully, so be sure that column is present, has no blanks, and is mapped to the id field in the settings.

The address fields in this step will be edit the primary address for the customer.

Delete Existing Customers

When using this option, every row in the step will be used to delete an existing customer, so be sure that your data is filtered down to the point that every row represents a customer to delete.

This step only requires a single field to be mapped - a column of Shopify customer IDs to delete. Make sure your data has a column of those IDs without any blanks. You can find the IDs by using the Pull from Shopify step.

Add Products to Collection

Collections allow shops to organize products in interesting ways! When using this option, every row in the step will be used to add a product to a collection, so be sure that your data is filtered down to the point that every row represents a product to add to a collection.

When using this option, any field that is not mapped in the settings table in the step will not be sent. Only the mapped fields will be sent to Shopify.

You only need two mapped fields for this option to work - a Shopify product ID and a Shopify Collection ID. Each row will essentially say, "Add this product to this collection".

Delete Product-Collection Relationships

Why is this option not called "Remove products from collections" if that is what it does? Great question. Products are kept in collections by creating a relationship between a product ID and a Collection ID. That relationship exists, and has its own ID! Imagine a spreadsheet full of rows that have product IDs and Collection IDs specifying which product belongs to which collections - each of those rows needs their own ID too. That ID represents the relationship. In fact, you don't need to imagine. Use the Pull from Shopify step to pull in Product-Collection Relationships. Notice there is an ID for each entry that is not the ID of the product or the collection. That ID is what you need to use in this step.

When using this option, every row in the step will be used to delete a product from a collection, so be sure that your data is filtered down to the point that every row represents a product-collection relationship that you want to remove.

This step does not delete the product or the collection! It just removes the product from the collection.

When using this step, any field that is not mapped in the settings table in the step will not be sent. Only the mapped fields will be sent to Shopify.

You need 1 field mapped for this step to work - it is the ID of the product-collection relationship, which you can find by Pulling those relationships in the Pull from Shopify step. In the step, it is called a "collect_id", and it is the "ID" column when you pull the product-collection relationships table.

Update Existing Inventory Items

What's an inventory item? Well, it represents the goods available to be shipped to a customer. Inventory items exist in locations, have SKUs, costs and information about how they ship.

There are a few aspects of an inventory item that you can update:

• ‍Cost: The unit cost associated with the inventory item - should be a number, such as 10 or 10.50‍
• SKU: Any string of characters that you want to use as the SKU for this inventory item‍
• Tracked: Whether the inventory item is tracked. Set this to true or false‍
• Requires Shipping: Whether a customer needs to provide a shipping address when placing an order containing the inventory item. Set this to true or false

When using this step, you need to provide an Inventory Item ID so that the step knows which Item you are trying to update. Remember, any field that is not mapped in the settings table in the step will not be sent. Only the mapped fields will be seny to Shopify.

“Update” option behavior

When using the “Update” option in the Send to Shopify step, Parabola will overwrite all existing values for any fields that are mapped in the step’s settings table. This behavior is standard for update requests and ensures that Shopify reflects the exact data provided in your flow.

Any fields not mapped will remain unchanged in Shopify. To avoid unintended data loss or partial updates, make sure to explicitly map all fields you want to update and double-check your input data before running the flow.

Adjust Existing Inventory Levels

When using this option, every row in the step will be used to adjust an existing item's inventory level, so be sure that your data is filtered down to the point that every row represents an item to adjust.

When using this step, any field that is not mapped in the settings table in the step will not be sent. Only the mapped fields will be sent to Shopify.

Every item must have a Shopify inventory item ID present in order to adjust successfully, so be sure that column is present, has no blanks, and is mapped to the id field in the settings.

You must provide the inventory item ID, the location ID where you want to adjust the inventory level, and the available adjustment number. That available adjustment number will be added to the inventory level that exists. So if you want to decrease the inventory level of an item by 2, set this value to -2. Similarly, use 5 to increase the inventory level by 5 units.

Reset Inventory Levels

When using this option, every row in the step will be used to reset an existing item's inventory level, so be sure that your data is filtered down to the point that every row represents an item to reset.

When using this step, any field that is not mapped in the settings table in the step will not be sent. Only the mapped fields will be sent to Shopify.

Every item must have a Shopify inventory item ID present in order to reset successfully, so be sure that column is present, has no blanks, and is mapped to the id field in the settings.

You must provide the inventory item ID, the location ID where you want to adjust the inventory level, and the available number. That available number will be used to overwrite any existing inventory level that exists. So if you want to change an item's inventory from 10 to 102, then set this number to 102.

To use the Pull from Shopify or Send to Shopify steps, you'll need to first authorize Parabola to connect to your Shopify store.

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To start, you will need your Shopify shop URL. Take a look at your Shopify store, and you may see something like this: awesome-socks.myshopfy.com - from that you would just need to copy awesome-socks to put into the first authorization prompt:

After that, you will be shown a window from Shopify, asking for you to authorize Parabola to access your Shopify store. If you have done this before, and/or if you are logged into Shopify in your browser, this step may be done automatically.

Parabola handles authorization on the flow-level. Once you authorize your Shopify store on a flow, subsequent Shopify steps you use on the same flow will be automatically connected to the same Shopify store. For any new flows you create, you'll be asked to authorize your Shopify store again.

Editing your authorization

You can edit your authorizations at any time by doing the following:

• Open your Pull from Shopify or Send to Shopify step.
• Click on the authorization dropdown near the top of the result view.

• Click on "Edit accounts" at the bottom of the dropdown.
• Click the three dots next to the Shopify Auth that you are currently using or want to edit.
• We recommend that you rename your Account Name so you can easily keep track of which Shopify store you're connected to.

Managing multiple Shopify stores in a single flow

If you manage multiple Shopify stores, you can connect to as many separate Shopify stores in a single flow as you need. This is really useful because you can combine data from across your Shopify-store and create wholistic custom reports that provide a full picture of how your business is performing.

Adding an authorization for another Shopify Store

• Open your Pull from Shopify or Send to Shopify step.
• Click on authorization dropdown at the top of the result view.
• Click on "Add new account" in the dropdown.

• Another authorization window will appear for you to authorize to a different store. Don't worry, connecting to a different store in one Shopify step will not impact the already-connected Shopify steps already on your flow.
• The "Edit Accounts" menu is how you can switch which account a step is pulling from or pushing to. We recommend renaming the Account Name of your various Shopify accounts so it's easier to toggle in between your different accounts.

Deleting a Shopify account from authorization

Please note that deleting a Shopify account from authorization will remove it from the entire flow, including any published versions.

• Open your Pull from Shopify or Send to Shopify step.
• Click on authorization dropdown near the top of the result view.
• Click Edit accounts.
• Click on the three dots next to the Shopify account that you'd like to remove authorization for and choose the delete option.

This article goes over the date filters available in the Pull from Shopify step.

The Orders and Customer pulls from the Pull from Shopify step have the most complex date filters. We wanted to provide lots of options for filtering your data from within the step to be able to reduce the size of your initial import and pull exactly the data you want to see.

Date filters can be a little confusing though, so here's a more detailed explanation of how we've built our most complex date filters.

The date filters in the Pull from Shopify step, when available, can be found the bottom of the lefthand side, right above the "Show Updated Results" button.

The first date filter you can set is:

• within the previous # day, hour, week, or month
• based on when the flow is run or the most recently completed day
• You can also add an offset to the previous period or previous year
• Example 1: If today is August 17, 2020, and I select within the previous 1 day based on the most recently completed day with no offset, the date range used would be August 16, 2020 12:00am PDT -August 17, 2020 12:00am PDT. Since August 16, 2020 was the most recently completed day, it's pulling in data from that day.
• Example 2: If today is August 17, 2020, and I select within the previous 1 week based on when the flow is run offset to the previous period, the date range used would be August 3, 2020 - August 10, 2020. This is temporarily calculated based on the assumption that I'll run my flow soon. It will be automatically recalculated at the time I actually run my flow. The previous one week from today would be August 10, 2020-August 17, 2020. Since I'm offsetting to the previous period (one week), the date range is pulling data from the week prior.
• Example 3: If today is August 17, 2020, and I select within the previous 1 month based on the most recently completed month offset to the previous year, the date range used is July 1, 2019 12:00am PDT - August 1, 2019 12:00am PDT. The most recently created month will be July 2020 and I want to pull data from that month. By offsetting to the previous year, I see data from July 2019.

The second date filter you can set is:

• within the current day to date, week to date, month to date, year to date
• You can add an offset to the previous period or previous year.
• Example 1: If today is August 17, 2020, and I select within the current month to date with no offset, the date range used will be August 1, 2020-August 17, 2020.
• Example 2: If today is August 17, 2020, and I select within the current year to date with offset to the previous period, the date range used will be January 1, 2019-August 17, 2019. The previous period in this situation is the same time frame, just the year before.
• Example 3: If today is Tuesday, August 17, 2020 and I select within the current week to date with offset to the previous year, the date range used will be August 16, 2019-August 17, 2019. Week to date is calculated from Sunday being the first day of the week. Offsetting to the previous year will take the same dates, but pull data from those date from the previous year.

The third date filter you can set is:

• after x date
• Example: after January 1, 2020

The fourth and last date filter you can set is:

• between x and y dates
• Example: between January 1, 2020 and June 30, 2020

Time zones

In this step, we indicate what time zone we're using to pull your data. This time zone matches the time zone selected for your Shopify store.

Confirming the date range

At the bottom of the lefthand panel of your step, if you're still uncertain if you've configured the date filters correctly, we have a handy helper to confirm the date range we'll use to filter in the step:

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This article explains how to reproduce the most commonly-used Shopify metrics. If you don't see the metric(s) you're trying to replicate, send us a note and we can look into it for you.

The Shopify Overview dashboard is full of useful metrics. One problem is that it doesn't let you drill into the data to understand how it's being calculated. A benefit of using Parabola to work with your Shopify data is that you can easily replicate most Shopify metrics and see exactly how the raw data is used to calculate these overview metrics.

Total Sales by line items

This formula will show you the total sales per line item by multiplying the price and quantity of the line items sold.

Import Orders with Line Items details

{Line Items: Quantity} * {Line Items: Price}

Total Refunds by line items

This formula will show you the total refund per line item by multiplying the refunded amount and refunded quantity. In this formula, we multiply by 1 to turn it into a negative number. If you'd like to display your refunds by line items as a positive number, just don't multiply by 1.

Import Orders with Line Items details

{Refunds: Refund Line Items: Quantity} * {Refunds: Refund Line Items: Subtotal}*-1

Net quantity

This formula will show you the net quantity of items sold, taking into account and removing the items that were refunded.

Import Orders with Line Items details

First, use the Sum by group  step to sum "Line Items: Quantity" and "Refunds: Refund Line Items: Quantity"

Then, use the newly generated "sum" columns for your formula.

{Line Items: Quantity (sum)}-{Refunds: Refund Line Items: Quantity (sum)}

Gross sales

Import Orders with Orders details.

Add a Sum by group step. Sum the "Total Line Items Price" column.

Net sales

Import Orders with Orders details.

To calculate net sales, you'll want to get gross sales - refunds - discounts. This will require two steps:

1. Add a Sum by group step and sum the following columns: "Total Line Items Price", "Total Refunded Amount", and "Total Discounts".
2. Add an Insert Math Column step and add in the following equation:

{Total Line Items Price (sum)}-{Total Refunded Amount (sum)}-{Total Discounts (sum)}

Total sales

Import Orders with Line Items details.

To calculate total sales, you'll want to get gross sales + taxes - refunds - discounts. This will require three steps:

1. Add an Insert math column step and add in the following equations to get gross sales and call the column, "Sales":

{Line Items: Quantity} * {Line Items: Price}

1. Add in a Sum by group step and sum the following columns: {Sales}, {Line Items: Total Discount Allocations}, {Refunds: Refund Line Items: Subtotal}, {Line Items: Total Tax Lines}, and {Refunds: Refund Line Items: Total Tax}.
2. Add in an Insert math column step with the following equation:

{Sales (sum)} + ({Refunds: Refund Line Items: Subtotal}*-1) - {Line Items: Total Discount Allocations (sum)} + ({Line Items: Total Tax Lines (sum)} - {Refunds: Refund Line Items: Total Tax (sum)})

Total refunds

Import Orders with Orders details.

• Add a Sum by group step and sum the following column: "Total Refunded Amount".

Total discounts

Import Orders with Orders details.

• Add a Sum by group step and sum the following column: "Total Discounts".

Total tax

Import Orders with Orders details.

• Add a Sum by group step and sum the following column: "Total Tax".

Average order value

Import Customers. This table will give us Total Spent per customer as well as the # of Orders by customer.

• Add a Sum by group step and sum the columns: {Orders Count} and {Total Spent}.
• Add an Insert math column step and use the following calculation:

{Total Spent (sum)} / {Orders Count (sum)}.

Alternatively, import Orders.

• Add an Insert math column step and create a new column called Orders with the following calculation: =1
• Add a Sum by group step and sum the columns: Orders and Total price
• Add an Insert math column step and create a new column called Average Order Value and use the following calculation:

{Total Price (sum)} / {Orders (sum)}

Number of orders

Use the Count by group step after pulling in orders.

Use the Send to Slack step to automatically post messages from your Parabola flow into a Slack channel or DM.

Setup & Authentication

The first person to install the Parabola Slack app in your workspace may need admin permissions. Once installed, all workspace members can use the app.

Your authentication process depends on your Slack workspace settings:

• If your workspace allows app installs, the Parabola app installs during authentication of the Send to Slack step.
• If not, you may need an admin to install it. Some workspaces provide an option to submit a request to your admin for approval.

Click on the button below to add the Parabola app to your Slack workspace or send this to your Slack admin to set-up.

To connect a Send to Slack step:

1. Drag a Send to Slack step onto your canvas.
2. Click the blue Connect to Slack button.
   If you’re connecting for the first time, select + Add new account. To update an existing account, click Edit Accounts.
3. If you’ve connected before, you’ll see available options in the dropdown for quick setup.
4. Review the permissions in the pop-up window and click Allow. If no window appears, check for a pop-up blocker.
5. If you’re already logged in to Slack, the step connects automatically. Otherwise, follow the login instructions to connect.

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Message Settings

• Message type:
  
  • Send a single message sends one message with your configured text.
    
  • Send one message per row sends a separate message for each row of data.
  • Select Channel message and choose a channel, or
  • Select Direct message to user and choose a user. 
    
    • Both channel messages and DMs send from the “Parabola app”, not your own Slack profile.
    • To direct message multiple users, duplicate the Send to Slack step, filter rows for each user, and configure each step separately.
    • Direct Messages sent with this integration will appear under the “Apps” section in your Slack sidebar.
• Message text: 
  
  • Write plain text or Slack markdown.
  • Reference column values dynamically with curly braces. For example: {SKU}.
  • When sending a single message, values from the first row fill in the curly-braced references.
• Message Settings Gear icon (all on by default):
  
  • Include a link to this flow (requires appropriate flow permissions for recipients).
  • Expand URLs and images in Slack
  • Link usernames and channels. Channels can be referenced as “#general” and users as @alex
• Sending test messages:
  
  • Click Send test message to send test messages to yourself without running the full flow. Test messages do not use Parabola credits.
• Attached file:
  
  • Do not attach anything is the default message. This means that the recipient will only receive the content configured in the Message Text box and a link to the flow if you kept this enabled in your Message Settings.
  • Attach entire table as a CSV - you’ll name the CSV file that will be sent via Slack. The individual file size limit is 1 GB, based on Slack’s file upload limit.
  • Attach a file by URL - Use this setting when you have a column that contains file URLs. Merge in that column’s value by wrapping the column name in curly braces. You can also enter a file URL manually if you have one stored elsewhere.

Formatting messages with markdown 

Basics

_italic_ will produce italicized text

*bold* will produce bold text

~strike~ will produce strikethrough text

Line breaks 

You can write multi-line text by typing a new line, or insert a newline by including the string “\n” in your text.

Block quotes 

You can highlight text as a block quote by using the > character at the beginning of one or more lines.

Code blocks 

If you have text that you want to be highlighted like code, surround it with back-tick (`) characters.For example:

`This is a code block`

You can also highlight larger, multi-line code blocks by placing 3 back-ticks before and after the block. For example: 

```This is a code block\nAnd it's multi-line```

Lists 

Create lists by using a - character followed by a space. For example:

- This

- is

- a list

Links 

URLs will automatically work. Spaces in URLs will break the URL, so we recommend that you remove any spaces from your URL links.

You can also use markdown to adjust the text that appears as the link from the URL to something else: For example:

<http://www.example.com|This message *is* a link>

And create email links:

<mailto:bob@example.com|Email Bob Roberts>

Emoji 

Emoji can be included in their full-color, fully-illustrated form directly in text. Once published, Slack will then convert the emoji into their common 'colon' format. For example, a message published like this:

It's Friday 😄

will be converted into colon format:

It's Friday :smile:

If you're publishing text with emoji, you don't need to worry about converting them, just include them as-is.

The compatible emoji formats are the Unicode Unified format (used by OSX 10.7+ and iOS 6+), the Softbank format (used by iOS 5) and the Google format (used by some Android devices). These will be converted into their colon-format equivalents. The list of supported emoji are taken from https://github.com/iamcal/emoji-data.

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Helpful Information

• You can preview messages by sending yourself a test DM before running the Flow.
• Slack messages have a 40k character limit.
• Some Slack features (like @here) aren’t supported.
• You cannot send Slack messages to private channels.
• If you’re posting into a channel, make sure the Parabola app has been added to that channel first (or ask a Channel Manager to add it).

The Pull from Slack step is a beta step and requires more  set-up compared to our Send to Slack step.

Authentication & getting started

1. Go to the Slack App creation page.
2. Click the Create New App dropdown in the top left.
3. Select From scratch.
4. Enter Parabola for your app name.
5. Choose the workspace where you want to use the API.
6. Click Create App

Set up OAuth permissions

• In the left-hand menu, click to OAuth & Permissions

• Scroll down to “Scopes”

• Click to Add an OAuth Scope
  
  • Select the permissions your app needs. For example:
    
    • For example, if you want to read messages in a channel, select 
      
      • channels:history
      • groups:history
      • im:history
      • mpim:history

You can add multiple scopes depending on what you want the app to do.

• Next, we’re going to install the App to your workplace. 
  
  • Scroll up to “OAuth Tokens” and select Install to…‍
  • Select Allow on the next page. 

Copy the Bot User OAuth Token on the previous page. You will need it within the Parabola step as the Bearer Token

Install the app in a Slack channel

• Open the Slack channel where you want to access the data
• Click the three dots to open Channel details.

• Go to the Integrations tab
• Click to Add an App
• Search for your app’s name
• Click Add to  channel.

Authenticate & pull in data from Parabola

• In Parabola, drag a Pull from API step onto your Flow.
• Find the endpoint that you’re looking to access. 
  
  • Full List of endpoints here: https://api.slack.com/methods 
  • To easily access Channel History, you can copy and paste this into your flow: parabola:cb:cecee323-f2e9-49d2-b15e-d1f1efcf9f23 
• Slack uses the “Bearer Token” access. 
  
  • Copy and paste the Bot User OAuth Token into the token location. 
  • For the above endpoint, please ensure you also add in the Channel ID (available in the same Channel Details as where you added the app)

Helpful tips:

• The endpoint will likely have required scopes that you’ll need to ensure you include!
• For example, for Conversations History: 

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The Pull from Smartsheet step enables you to pull in data from Smartsheet (a collaborative spreadsheet tool) used for organizing and working with data. This way, you'll be able to view your data as a table, workflow, or timeline and automate the process of making reports. You may also combine with other data sources.

Connect your Smartsheet account

To authorize your Smartsheet account in this step, select Authorize.

Then, a new webpage tab will open and redirect you to log into your Smartsheet account. Once you login, select Allow to finalize the authorization.

After this, your webpage will return to the tab with your Parabola flow on it and refresh the step automatically.

Custom settings

The step will automatically select and pull in the first Sheet listed in your Smartsheet account's Sheets section. To bring in a different Smartsheet Sheet with the dataset you'd like to work with, select the name of the sheet to pull it in and click on the circular arrow icon next to the step's name Pull from Smartsheet to refresh the display window.

After a dataset from your Smartsheet sheet is pulled in, select the blue  "Show Updated Results" button to save these settings in the step.

Helpful tips

• Any changes you make in Smartsheet Sheets will automatically sync with Parabola in your steps that incorporate those datasets.

The Send to Smartsheet step enables you to automate data entry in Smartsheet, automatically add new data into existing Sheets, send reports to customers and clients, or add new data to existing Sheets.

Connect your Smartsheet account

To authorize your Smartsheet account in this step, select Authorize.

Then, a new webpage tab will open and redirect you to log into your Smartsheet account. Once you login, select Allow to finalize the authorization.

After this, your webpage will return to the tab with your Parabola flow on it and refresh the step automatically.

Custom settings

Select the Sheet you'd like to overwrite and update, or select Create New Sheet to make a new one in Smartsheet.

Select Show Updated Results to save the step settings and update the display window.

Under the Map "column name" to field type: settings section, you may also select one of 11 field types to customize a column's field type in Smartsheet.

Helpful tips

• Any data in your step's result view will be used to overwrite the data in that Smartsheet.

Use the Pull from Snowflake step to pull in your data from your Snowflake database.

This step is currently offered to users on our Advanced Plan. Check out the Pricing Page for additional information.

Before you get started, check to see if your team has already set up their Client ID and Client Secret for Parabola. If you or someone else on your team has already set this up on the Snowflake side, you will not need to go through this process again and can jump straight to the Parabola Step Set Up section**.**

Connect your Snowflake account

In order to perform these steps, you must have the right permission level in Snowflake to create a security integration.

Login to your Snowflake account. Once you’re logged in, click on “Worksheets” tab in the sidebar and click + Worksheet button in the upper right hand corner

In the worksheet, paste the query below into the worksheets query box. This will instantiate a custom client OAuth server on your Snowflake instance that Parabola will use to connect to.

create security integration oauth_parabola_prod
type = oauth
enabled = true
oauth_client = custom
oauth_client_type = 'CONFIDENTIAL'
oauth_redirect_uri = 'https://parabola.io/api/auth/snowflake/callback'
oauth_issue_refresh_tokens = true
oauth_refresh_token_validity = 7776000

The configuration above is the basic default settings for OAuth server up, but can be customized further for your needs. Additional information located on Snowflake documents here.

Click the Run/Play button.  If successful, you should see a notification on the lower portion of the screen confirming integration creation was successful.

Retrieving the Client ID and Client Secret

Run the following query:

select system$show_oauth_client_secrets('OAUTH_PARABOLA_PROD');

Note: The name of your integration passed into this statement should be all capitalized. Ex “oauth_parabola_prod” should be entered as 'OAUTH_PARABOLA_PROD'

Click on the result in the lower half of the page and copy the oauth_client_id and oauth_client_secret values in the resulting json

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Parabola Step Set Up

In your builder, bring in the Snowflake step and Click on “Authorize Snowflake”. You will see a form asking for client_id, client_secret, and account_identifier.  For client_id and client_secret, paste the values you received above.

For account_identifier, paste your Snowflake account id.  Your account ID will be the located in your URL:

<account_identifier>.snowflakecomputing.com

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If your Snowflake URL has a region included in it, along with an account identifier, you may need to include that region as well in this step.

After you hit Submit, a module will pop up which will ask to authenticate. Login to your Snowflake account as you always would. After logging in, you should be taken back to Parabola. You will now be able to query data from Snowflake!

Step Permissions

When a user authorizes our "Pull from Snowflake" step, their access to data within the Parabola step will be the same as their access to data within the Snowflake platform. If a user has granular permissions configured in Snowflake, their access will be gated in the same fashion within Parabola.

While credentials like Client ID and Client Secret are at the organization level, when a user actually authenticates the step through their Snowflake login, we ensure that the actual user account permissions are enforced within the step itself.

Parabola now supports shared authentication for Snowflake, making it easier for teams to authorize Snowflake once and reuse that connection across flows and users—without needing to repeatedly input credentials or reauthorize each step.

• Admins can authorize a Snowflake connection once via any Pull from Snowflake or Send to Snowflake step.
• Any team member can select the shared Snowflake connection in their Pull from Snowflake or Send to Snowflake step by choosing the appropriate shared auth profile.
• Permissions and access levels will still reflect the Snowflake role tied to that authenticated connection.

Learn more about managing integration accounts here.

Helpful Tips

On pulling data from Snowflake

There is currently no way to add dynamic filters to your query, so you’ll want to filter your data as much as possible in the query itself to limit the amount of data you’re pulling in. 

Snowflake's maximum token validity

The maximum token validity is 90 days, as enforced by Snowflake. This limit cannot be extended. You can set a shorter validity period by updating the oauth_refresh_token_validity parameter for your custom client. If a token expires after reaching its maximum validity, you will be prompted to reauthorize your Snowflake connection the next time the flow runs.

Snowflake permissions

By default Parabola will mimic the permissions you have within your Snowflake instance. The request will check the user's default role, warehouse, and database/schema. If these values are not set, or the users default values are not sufficient to make a certain request, you will see an error message like below:

Settings Error: Error occurred with Snowflake API (status_code: 422, message: “SQL compilation error: Object ‘CUSTOMER’ does not exist or not authorized.”)

If this occurs, open up the settings on the left-hand side labeled Connection Options and manually enter the values you would like to use to make a query:

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You can play around with these values in the Snowflake worksheets section to find a configuration that works for you. In the upper left hand corner of the page select for role or warehouse, and the sidebar for database or schema respectively:

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Use the Send to Snowflake step to insert, update, or merge data into your Snowflake database.

This step is currently offered to users on our Advanced Plan. Check out the Pricing Page for additional information.

Before you get started, check to see if your team has already set up their Client ID and Client Secret for Parabola. If you or someone else on your team has already set this up on the Snowflake side, you will not need to go through this process again and can jump straight to the Parabola Step Set Up section.

Connect your Snowflake account

In order to perform these steps, you must have the right permission level in Snowflake to create a security integration.

Login to your Snowflake account. Once you’re logged in, click on “Worksheets” tab in the sidebar and click + Worksheet button in the upper right hand corner

In the worksheet, paste the query below into the worksheets query box. This will instantiate a custom client OAuth server on your Snowflake instance that Parabola will use to connect to.

create security integration oauth_parabola_prod

The configuration above is the basic default settings for OAuth server up, but can be customized further for your needs. Additional information located on Snowflake documents here.

Click the Run/Play button.  If successful, you should see a notification on the lower portion of the screen confirming integration creation was successful.

Retrieving the Client ID and Client Secret

Run the following query:

select system$show_oauth_client_secrets('OAUTH_PARABOLA_PROD');

Note: The name of your integration passed into this statement should be all capitalized. Ex “oauth_parabola_prod” should be entered as 'OAUTH_PARABOLA_PROD'

Click on the result in the lower half of the page and copy the oauth_client_id and oauth_client_secret values in the resulting json

Parabola Step Set Up

In your Flow builder, add the Send to Snowflake step and click on “Authorize Snowflake”. You will see a form asking for client_id, client_secret, and account_identifier.  For client_id and client_secret, paste the values you received above.

For account_identifier, paste your Snowflake account id.  Your account ID will be the located in your URL:

<account_identifier>.snowflakecomputing.com

If your Snowflake URL has a region included in it, along with an account identifier, you may need to include that region as well in this step.

After you hit “Submit”, a window will pop up which will ask to authenticate. Login to your Snowflake account as you always would. After logging in, you should be taken back to Parabola. You will now be able to send data to Snowflake!

Step Permissions

When a user authorizes our Send to Snowflake step, their access to data within the Parabola step will be the same as their access to data within the Snowflake platform. If a user has granular permissions configured in Snowflake, their access will be gated in the same fashion within Parabola.

While credentials like Client ID and Client Secret are at the organization level, when a user actually authenticates the step through their Snowflake login, we ensure that the actual user account permissions are enforced within the step itself.

Sending data

This step can send data in 3 different ways:

• Insert - adds new rows to Snowflake
• Update - uses a unique identifier to find existing rows to update
• Merge (upsert) - attempts an update, and if no matching rows are found, inserts the data instead

Both update and merge require a Snowflake column to be used as the unique identifier.

This step cannot create or remove tables within Snowflake. A database table must already exist in Snowflake, with a schema of columns, to use this step.

Any columns within Parabola that are not mapped to corresponding columns in Snowflake will not be sent. If any Snowflake columns do not have corresponding columns mapped within Parabola, the resulting new rows will have blank values in those columns.

⚠️ Note: when using the “update” option, Snowflake will not send back an error if an update was unable to find a matching row. The Parabola Flow will indicate success and look like it sent a number of rows, but if any of those rows during the update process were unable to match any rows in Snowflake, no error will be returned. This is unfortunately a Snowflake limitation.

Helpful Tips

By default Parabola will mimic the permissions you have within your Snowflake instance. The request will check the users default role, warehouse, and database/schema.  If these values are not set, or the users default values are not sufficient to make a certain request, you will see an error message like below:

Settings Error: Error occurred with Snowflake API (status_code: 422, message: “SQL compilation error: Object ‘CUSTOMER’ does not exist or not authorized.”)

If this occurs, try updating the Role, Warehouse, Database, or Schema settings.

You can play around with these values in the Snowflake worksheets section to find a configuration that works for you. In the upper left hand corner of the page select for role or warehouse, and the sidebar for database or schema respectively:

Snowflake's maximum token validity

The maximum token validity is 90 days, as enforced by Snowflake. This limit cannot be extended. You can set a shorter validity period by updating the oauth_refresh_token_validity parameter for your custom client. If a token expires after reaching its maximum validity, you will be prompted to reauthorize your Snowflake connection the next time the flow runs.

The Pull from Square step connects directly to your data in Square. Pull in data on transactions, refunds, customers, locations, and more.

Connect your Square account

To connect your Square account to Parabola, double-click on the Pull from Square step and click "Authorize." A window will pop up asking you to sign in to your Square account using your email and password. Once you complete the login, you'll see the step on Parabola connected and pulling in your data.

Default settings

When you first connect to the Pull from Square step, it'll pull in Location Details which is the first option in the data type dropdown.

If you click into "Advanced Settings," you can filter locations if you have multiple locations and you want to filter to see data for those particular locations.

Custom settings

Here are the available data sets in the data type dropdown:

• Location Details (see default settings)
• Transactions
• Refunds
• Catalog
• Customers
• Employees

Transactions

Pulling in Transactions data will return the following columns:

• "created_at"
• "transaction_id"
• "device.id"
• "device.name"
• "tax_money.amount"
• "total_collected_money.amount"
• "net_sales_money.amount"
• "location_id"

By default, this option will pull in all data for your selected time frame. However, you can filter for the following subsets of data: Tenders, Refunds, Line Items, Transactions Report, and Item Details Report.

The Timeframe will default to the Last 7 Days, but the following timeframe options are available: Last 24 Hours, Last 1 Day, Last 7 Days, Last 30 Days, Last Month, Last 3 Months, Last 6 Months, Last Year, This Year, and Custom Range.

If you select the Custom Range option, you can configure a Start Date and End Date. Please make sure to provide these dates in the following format: MM-DD-YYYY. So, February 28, 2020 will be indicated as 02-28-2020.

You should also set the appropriate Time Zone to use to filter for your dates. By default, the Africa/Abidjan time zone will be selected since that's the first time zone listed in our alphabetical list.

If you click into "Advanced Settings," you'll see an option to Filter Locations if it'd be useful to filter your data by one or many locations.

You can also adjust the offset of your relative timeframe by customizing how many days, weeks, or months ago we should start the timeframe from.

You can also specify a Day Start Time which will be 12:00AM as a default.

Refunds

Pulling in Refunds data will return the following columns:

• "created_at"
• "transaction_id"
• "device.id"
• "device.name"
• "tax_money.amount"
• "total_collected_money.amount"
• "net_sales_money.amount"
• "location_id"

By default, this option will pull in all data for your selected time frame. However, you can filter for the following subsets of data: Original Transaction Tenders, Original Transaction Line Items, Refunds Report, Item Details Report.

The Timeframe, Time Zone, and Advanced Settings are all the same as the Transactions data type above.

Catalog

Pulling in Category data will return your item catalog including items, variations, categories, discounts, taxes, modifiers, and more. A total of 92 columns are returned.

Customers

Pulling in Customers data will return the following columns:

• "id"
• "created_at"
• "updated_at"
• "given_name"
• "family_name"
• "email_address"
• "reference_id"
• "preferences.email_unsubscribed"
• "groups[0].id"
• "groups[0].name"
• "address.address_line_1"
• "address.locality"
• "address.administrative_district_level_1"
• "address.postal_code"
• "phone_number"

Employees

Pulling in Employees data will return the following columns:

• "authorized_location_ids[0]"
• "authorized_location_ids[1]"
• "id"
• "first_name"
• "last_name"
• "status"
• "authorized_location_ids[2]"
• "role_ids[0]"
• "email"

If you click into "Advanced Settings," you can filter locations if you have multiple locations and you want to filter to see data for those particular locations.

Helpful tips

• Timeframes will always shift to only include full units of time. If you choose the last 7 days, it will begin with the most recent full day, not the partial day you are in right now. If you choose the last 3 months, it will begin with the most recent full month, not including the partial month you are in right now.
• Inventory is not currently a supported category in the native Square integration, but you can attempt to connect Square’s API directly to pull in inventory data.

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The Pull from Squarespace step pulls data from your Squarespace account via their API.

The Pull from Squarespace step is a beta step. It is a Pull from an API step that has been pre-configured to work with the Squarespace API.

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NOTE: Squarespace requires an "Advanced Commerce" plan to pull data from their Commerce API. For additional information, please visit their pricing page.

Connect your Squarespace account

Connecting to the Squarespace API is straightforward. You will need to provide an API Key from your Squarespace account. Head here for instructions from Squarespace on generating an API key.

Once you have your API Key, add it into the step into the Bearer Token field.

If the pull does not bring back all of your data, increase the Max Requests field so that more pages are fetched.

Custom settings

This beta step is pre-configured to pull data in from the Squarespace Orders endpoint. You can update the URL in the API Endpoint URL field if you'd like to access data from a different endpoint. You can view all available endpoints from Squarespace's Commerce API here.

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The Pull from Stripe step connects to your Stripe account and pulls the following data types into Parabola in a familiar spreadsheet format:

• coupons
• customers
• invoices
• payments
• plans
• products
• subscriptions

Connect your Stripe account

Double-click on the Pull from Stripe step and click "Authorize." A pop-up window will appear asking you to log in to your Stripe account to connect your data to Parabola

If you ever need to change the Stripe account that your Parabola flow is connected to, click "Edit accounts" at the top of the step and select to either "Edit" or "Add new account." Both options will prompt the same Stripe login window to update or add a new account.

Custom settings

The first thing you'll want to do is select a data type to pull in from Stripe. Below are the seven different data types available.

Coupons

See data about coupons existing in your Stripe account. Please note that Stripe returns "Amount Off" with no decimals, so if you see 50000 in the "Amount Off" column, that will equal 500.00. You can connect our Insert math column step and Format numbers to update this if you prefer.

Column headers:

• "id"
• "Duration"
• "Duration in Months"
• "Percent Off"
• "Amount Off"
• "Currency"
• "Redeem By"
• "Max Redemptions"

Customers

See data about your customers in your Stripe account. The Created field displays when the customer was created in Stripe. The time is represented in Unix time. You can connect our Format dates step to update the date to your preferred format.

Column headers:

• "id"
• "Description"
• "Email"
• "Email"
• "Created"
• "Delinquent"
• "Plan ID"
• "Plan Name"
• "Quantity"
• "Status"
• "Balance"

Invoices

See data about invoices that exist in your Stripe account. The "Created" field displays when the invoices were created in Stripe. The time is represented in Unix time. You can connect our Format dates step to update the date to your preferred format.

Column headers:

• "id"
• "Created"
• "Customer ID"
• "Amount Due"
• "Total"
• "Tax"
• "Currency"
• "Charge ID"
• "Subscription ID"
• "Coupon"
• "Attempt Count"
• "Closed"

Payments

See data about payments that exist in your Stripe account. The "Created" field displays when the payment was created in Stripe. The time is represented in Unix time. You can connect our Format dates step to update the date to your preferred format. Please note that Stripe returns Amount with no decimals, so if you see 50000 in the Amount column, that will equal 500.00. You can connect our Insert math column step and Format numbers to update this if you prefer.

Column headers:

• "id"
• "Customer ID"
• "Created"
• "Amount"
• "Amount Refunded"
• "Currency"
• "Order ID"
• "Invoice ID"
• "Application ID"
• "Application Fee"
• "Status"

Plans

See data about plans that exist in your Stripe account. The "Created" field displays when the plan was created in Stripe. The time is represented in Unix time. You can connect our Format dates step to update the date to your preferred format. Please note that Stripe returns Amount with no decimals, so if you see 50000 in the Amount column, that will equal 500.00. You can connect our Insert math column step and Format numbers to update this if you prefer.

Column headers:

• "id"
• "Name"
• "Product ID"
• "Created"
• "Interval"
• "Interval Count"
• "Amount"
• "Currency"
• "Trial Period"

Products

See data about products that exist in your Stripe account. The "Created" field displays when the plan was created in Stripe. The time is represented in Unix time. You can connect our Format dates step to update the date to your preferred format.

Column headers:

• "id"
• "Name"
• "Type"
• "Created"
• "Description"
• "Statement Descriptor"
• "Unit Label"
• "URL"

Subscriptions

See data about subscriptions that exist in your Stripe account. The "Created" date field returned is represented in Unix time. You can connect our Format dates step to update the date to your preferred format.

Column headers:

• "id"
• "Customer ID"
• "Plan ID"
• "Plan"
  "Name"
• "Interval"
• "Amount"
• "Status"
• "Coupon"
• "Created"
• "Start"
• "Current Period Start"
• "Current Period End"

Custom timeframe and time zone

For every data type available in the Pull from Stripe, we support the ability to customize the timeframe to use to pull the relevant data as well as the time zone we should use for the selected timeframe. Parabola will retrieve rows of data that were created within your selected timeframe.

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Get a full-picture view of your marketing performance across channels by adding TikTok data to your automated reports. Track key metrics like clicks, impressions, and payments, and combine your spend across platforms for a blended CAC metrics.

TikTok is a beta integration which requires a more involved setup process than our native integrations (like Facebook Ads and Google Analytics). Following the guidance in this doc should help even those without technical experience pull data from TikTok. If you run into any questions, shoot our team an email at support@parabola.io.

Connect your TikTok account

To pull marketing data from TikTok, you must start by registering as a TikTok developer through their Marketing Portal.

Once registered, you can then 'Create a Developer App.' Heads up – TikTok says this app may take 2-3 business days for them to review and approve. 

• Note: when you're creating the developer app, you'll be asked to provide a 'redirect URL.' You can supply the following URL: https://parabola.io/api/steps/generic_api/callback

With your developer app approved, you'll be provided with an auth_token URL that generates your access token. If you click on this URL or paste it into a new browser tab and search, you'll see an access token appended to the resulting URL. That access token can be copied and inserted into the "Pull from TikTok" step in the "Request Header" section.

You'll also need to acquire your "Advertiser ID", which can be pasted in the "Input Advertiser ID" card.

Custom Settings

Our TikTok integration was built to support TikTok's Basic Reports and Audience Reports. To help you get started, we've brought in a list of all the Metrics (ex. Spend, CPM) and Dimensions (ex. group by Campaign and Day) supported in TikTok's reports.

To start outputting your data once you've successfully set up your TikTok Developer Account, you'll need to follow 4 steps: 

1. In the "SELECT DIMENSIONS" and "SELECT METRICS" steps, select your desired data. These selections flow into the live API call later in the flow.

2. In the "Specify Date Range" card, follow the instructions on the card to set your desired date range.
3. Input your Advertiser ID in the corresponding step – this also will be dynamically inserted into your live API call
4. Enter your Access_Token in the "Pull from TikTok" step in the "Request Headers" section

Helpful Information

• Metric/ Dimension Groupings: There are certain dimension/ metric groupings that TikTok does not support (ex. in each request, you can only have one ID dimension and one time dimension). For a full explanation of their API capabilities, check out their report pages.
• Pulling additional data: If you're hoping to pull a different dataset from TikTok's API, check out their API documentation.

The Pull from Twilio step pulls messages and phone numbers from Twilio.

Connect your Twilio account

The first thing you'll need to do to start using the Pull from Twilio step is to authorize the step to access the data in your Twilio account.

Double-click on the step and click "Authorize." This window will appear where you'll need to provide the Account SID and Auth Token from your Twilio account.

To locate this information on your Twilio account, click on the blue link to Lookup Twilio Account Info. This will take you to https://www.twilio.com/console. You'll see your Account SID and Auth Token that you can copy and paste from your account to Parabola.

Custom settings

Once you're connected, you'll have the following data types to select from:

• Outbound Messages
• Inbound Messages
• Phone Numbers

Outbound Messages

This option pulls logs of all outbound messages you sent from your Twilio account. The returned columns are: To (phone number), From (phone number), Status, Price, Date Sent, Body (of message).

You have optional fields you can set to filter the data. Leaving the Date Sent field blank will simply pull in the most recent 100k messages.

Inbound Messages

This option pulls logs of any responses or inbound messages you've receive to the phone numbers associated with your Twilio account. The returned columns are: To (phone number), From (phone number), Status, Price, Date Sent, Body (of message).

You have optional fields you can set to filter data. Leaving the Date Received field blank will simply pull in the most recent 100k messages.

Phone Numbers

This option pulls in phone numbers that are associated with your account. The returned columns are: Number ID, Phone Number, Friendly Name, SMS Enabled, MMS Enabled, Voice Enabled, Date Created, Date Updated.

The Send to Twilio step triggers dynamic SMS messages sent via Twilio using data transformed in your Parabola flow. You can use Parabola to dictate who should receive your SMS messages, what message they should receive, and trigger Twilio to send them.

Connect your Twilio account

The first thing you'll need to do to start using the Send to Twilio step is to authorize the step to send data to your Twilio account.

Double-click on the step and click on the blue button to Authorize. This window will appear where you'll need to provide the Account SID and Auth Token from your Twilio account.

To locate this information on your Twilio account, click on the blue link to Lookup Twilio Account Info. This will take you to https://www.twilio.com/console. You'll see your Account SID and Auth Token that you can copy and paste from your account to Parabola.

Custom settings

By default, this step will be configured to Send text messages to recipients when the flow runs. If for whatever reason you need to disable this temporarily, you can select to not send text messages when the flow runs.

Then, you'll select the following columns that contain the data for phone numbers you'd like to Send To, phone numbers you'd like to Send From, and text you'd like Twilio to send as Message Content.

Please make sure that the phone numbers you'd like to Send From are valid Twilio phone numbers that your Twilio account is authorized to send from.  Verified Caller ID phone numbers cannot be used to send outbound SMS messages.

For Message Content, you have the option to use content from an existing column or a custom message. Select the Custom option from the dropdown if you'd like to type in a custom message. While the custom message is a great, easy option, this means that all of your recipients will receive the same message. If you'd like your messages to be customized at all, you should create your dynamic messages in a column beforehand. The Insert column can be particularly useful here for creating dynamic text content.

Each row will represent a single SMS. If your data contains 50 rows that means 50 SMS messages will be sent.

Helpful tips

• Twilio will charge you according to your account per message. You can monitor your Twilio usage by heading to Twilio's Console page.
• Twilio has a rate limit on sending messages. They will only send as fast as one per second, or 60 per minute. If your flow is attempting to send a large number of messages, be aware that it may run for a long time to comply with this limit.
• Parabola doesn’t automatically run the Flow upon each text, but you can pull in the texts based on some time/data parameters if you choose to schedule the Flow. It’s also possible, on theTwilio side, to set up a webhook that spins every time a text is sent, which can then be set to trigger the Flow via Parabola.

The Pull from Typeform step enables you to connect to your Typeform account and pull response data from your Typeform forms into Parabola.

Connect your Typeform account

Double-click on the Pull from Typeform step and click the blue button to Authorize. A pop-up window will appear asking you to log in to your Typeform account and connect your data to Parabola.

If you ever need to change the Typeform account that your Parabola flow is connected to, click "Edit accounts" at the top of the step and select to either "Edit" or "Add new account." Both options will prompt the same Typeform login window to update or add a new account.

Custom settings

The first thing you'll be asked to do is select the relevant Typeform form you'd like to pull in. Click on the "Form" dropdown on the lefthand side and you'll see all of the forms you have created in Typeform.

By default, the checkbox below to Include metadata from responses will be unchecked. With this option unchecked, a column will be created for every survey question, and a row of answers will appear for every response you receive.

If you check the box to Include metadata from responses, Parabola will also pull in metadata about a client's HTTP request that Typeform collected along with their responses. The following columns will be pulled into Parabola in addition to the question columns:

- "landing_id"
- "token"
- "response_id"
- "landed_at"
- "submitted_at"
- "hidden"
- "calculated"
- "user_agent"
- "platform"
- "referrer"
- "network_id"
- "browser"

Helpful tips

• Pulling data from multiple Typeform forms: if your flow requires data from multiple Typeform forms or multiple Typeform accounts, you can either duplicate or drag in another Typeform step, connect that step to a different form, or click edit accounts to connect to a different Typeform account entirely. Each Pull from Typeform step you have in a single Parabola flow can represent a different Typeform form. There is no limit to how many different forms' data that can be pulled in on a single Parabola flow.

The UPS API is used by businesses and developers to integrate UPS’s shipping, tracking, and logistics services into their platforms and workflows.

UPS is a beta integration which requires a slightly more involved setup process than our native integrations. Following the guidance in this document should help even those without technical experience pull data from UPS. If you run into any questions, shoot our team an email at support@parabola.io.

Use Cases

🤝 UPS | Integration configuration

📖 UPS API Reference:

https://developer.ups.com/catalog?loc=en_US

🔐 UPS Authentication Documentation:

https://developer.ups.com/api/reference?loc=en_US#tag/OAuth-Auth-Code

Instructions

1. Navigate to the UPS Developer Portal.

2. Click Login to access your UPS account.

3. Click Create Application to make a new application and generate your credentials.

⚠️ Note: This application will be linked to your shipper accounts(s) and email address associated with your UPS.com ID

4. Select your use case, shipper account, and accept the agreement.

5. Enter your contact information.

💡 Tip: Consider using a group inbox that is accessible to others on your development team. You are unable to change this email once the credentials are created or you will lose access to your application.

6. Define your application details that includes the name, associated billing account number, and custom products.

⚠️ Note: In the Callback URL field, add the following URL: https://parabola.io/api/steps/generic_api/callback

7. Once saved, your Client ID and Client Secret are generated.

💡 Tip: Click Add Products to enable additional products like the Tracking and Time in Transit APIs if they have not been added to your application.

8. Configure an OAuth 2.0 request to the OAuth Code endpoint in Parabola.

🔐 Parabola | Authentication configuration

1. Add an Enrich tracking from UPS step template to your canvas.

2. Click into the Enrich with API: UPS Tracking step to configure your authentication.

3. Under the Authentication Type, select OAuth 2.0 before selecting Configure Auth.

4. Toggle on Switch to custom settings.

5. Enter your credentials to make a request to the OAuth Code endpoint using the format below:

Auth Name

Give your authorization account an identifiable name.

Authorization Request URL (GET)

Test URL

https://wwwcie.ups.com/security/v1/oauth/authorize

Production URL

https://onlinetools.ups.com/security/v1/oauth/authorize

URL Parameters

Access Token Request URL (POST)

Test URL

https://wwwcie.ups.com/security/v1/oauth/token

Production URL

https://onlinetools.ups.com/security/v1/oauth/token

Body Parameters

Request Headers

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💡 Tip: You can configure an Authorization Header Value using a base-64 encoder. Encode your Client ID and Client Secret separated by a colon: Client ID:Client Secret.

In Parabola, use the Header Value field to type Basic , followed by a space, and paste in your encoded credentials: Basic {encoded credentials here}.

Refresh Token Request URL (POST)

Test URL

https://wwwcie.ups.com/security/v1/oauth/refresh

Production URL

https://onlinetools.ups.com/security/v1/oauth/refresh

Body Parameters

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Request Headers

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6. Click Apply custom Oauth 2 settings and a new window will appear.

7. Enter your email address, authorize Parabola to access the data, and click Continue to complete the authorization process.

Example Screenshot

🌐 UPS | Sample API Requests

Track UPS shipment statuses by tracking number

Get started with this template.

1. Add a Use sample data step to your Flow. You can also import a dataset with tracking numbers into your Flow (Pull from Excel File, Pull from Google Drive, Pull from API, etc.)

💡 Tip: When using your own data, use the Edit columns step to rename the tracking column in your source data to Tracking Number.

2. Connect it to an Enrich with API: UPS Tracking step.

3. Under Authentication Type, ensure OAuth 2.0 is selected to use your authentication credentials.

4. Click into the Request Settings to configure your request using the format below:

API Endpoint URL

Request Headers

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6. Click Refresh data to display the results.

Example Screenshot

The Visualize step is a destination step used to display data as charts, styled tables, or key metrics. These visualizations can optionally be shown on the Flow canvas or on the Flow dashboard.

Set up

When first added to your Flow and connected to a step, the Visualize step will expand. Data flowing into the Visualize step will be shown as a table on the canvas.

To customize this visualization and create new views, open the Visualize step by clicking "Edit this View."

Configuring views

Visualize steps can be configured with any number of views. Every view in a single Visualize step will use the same input data, but each view can be customized to  display data in a different way.

Syncing views to the Flow dashboard

The Visualize step is also used to sync views to your Flow dashboard tab. When the “Show on dashboard” step option is enabled, that  visualization will also appear in your Flow dashboard.

Views in the Visualize step will be shown on your Flow dashboard by default. Uncheck the dashboard setting within the Visualize step to remove any views from the dashboard.

Resizing, expanding and collapsing

Visualize steps can be collapsed into normal-sized steps by clicking the collapse button, located in the top right of the expanded visualization. Similarly, collapsed Visualize steps can be expanded by clicking on the expand button under the step.

Expanded Visualize steps can be resized using the handle in the bottom right of the step.

Flow dashboards enable your team to easily view, share, and analyze the data that your Flows create. Use the Visualize step to create interactive reports that are shareable with your entire team. Visualizations can be powered by any step in your Flow or by Parabola Tables for historic reporting.

Check out this Parabola University video for a brief intro to tables.

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‍How it works

The Visualize step is a tool for creating tables, charts, and metrics from the output of your Flows. These views of data can be arranged and shared directly in Parabola from the Flow dashboard page.

To create a Visualization, connect any step in your flow to a Visualize step:

Data connected to a Visualize step will be usable to create any number of views. Those views are automatically added to your Flow dashboard, where they can be arranged and customized.

Once you’ve added views to your Flow dashboard, you can:

• Visualize your data in the form of tables, featured metrics, charts, and graphs.
• Arrange a dashboard of multiple views, utilizing a tabbed or tiled layout.
• Analyze the entire page of views using quick filters.

Sharing tables with teammates

Anyone with access to your Flow will be able to see the Flow dashboard:

• "Can edit": any teammate with edit permissions can create and edit data views. Any changes to views will be visible immediately to anyone else who has access to the Flow.
• "Can view": teammates with view permissions can see all data views, but cannot make changes.

To share a view, you can either share the entire dashboard with your teammate (see instructions here), or click “Share” from a specific table view. Sharing the view will give your teammate access to the Flow (and it’s dashboard), and link them directly to that specific view.

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Sharing dashboards outside your team (external sharing)

Any visualization can be exported as a CSV. Simply click on the "Export to CSV" button at the top right of your table or chart.

Views are individual visualizations, accessible from the Visualize step, or on the Flow dashboard. The data connected to a Visualize step acts as a base dataset, which you can customize using views. Views can be visualized as tables, featured metrics, charts, and graphs.

Ready for a deeper dive? This Parabola University video will walk you through some of the configurations available to fine-tune how you see your data.

Page layout

Arrange data views on the page with either a tab or tile layout.

Tabs will appear like traditional spreadsheet tabs, which you can navigate through. Drag to rearrange their order.

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Tiles enable you to see all views simultaneously. You can completely customize the page by changing view height and width, and drag-and-drop to rearrange.

Helpful tips:

• Views will refresh their results if: the Flow runs, the base data is updated, and/or settings are changed
• Click the overflow menu next to the name of a view to move, rename, duplicate, or delete it. Use the same menu to switch the page layout between tabs and tiles
• Add new views by clicking the plus icon to the right of the last tab view, or by clicking the large “Add view” button below the last tile view. If you have too many tab views to see the icon, use the tab list menu on the right side of the table
• Duplicated and new tab views will show up in the private views section, so you may need to scroll down to see your new view

From the “Table/chart options” menu, you can select from several types of visualizations.

Tables

By default, visualizations display as tables. This format works well to show rows of data that are styled, calculated, grouped, sorted, or filtered.

In the below image, the table options menu is at the top left, below the "All Inventory" tab. This is where you can access options to format and style columns, or to add aggregation calculations.

Featured metrics

Featured metrics allow you to display specific column calculations from the underlying table.

Metrics can be renamed, given a color theme, and formatted (date, number, percent, currency, or accounting). The metrics options menu is in the same placement as above, represented with a '#' symbol.

Charts and graphs

Parabola supports several chart types:

• Column chart
• Line chart
• Area chart
• Scatter chart
• Mixed chart (multiple types combined)

Within the chart options menu, represented below as a mini bar graph, you can customize chart labels, color themes, gridlines, and legend placement.

X axis