Free template: find and replace values within your Shopify data
Find and replace values within your Shopify data without writing a single line of code.
The Parabola Team
Trusted by the fastest moving teams at hundreds of leading brands
Sick of manual SOPs?
Sign up for a free trial of Parabola and start automating your most manual work today.
Sign up for freeTransform your data in five easy steps using Parabola's drag-and-drop interface, powered by AI.
- Set up your data source by creating a new Parabola flow and connecting your Shopify store.
- Select the specific fields where you need to perform find and replace operations. Configure any necessary filters.
- Use Parabola's text transformation tools to specify your search and replacement rules. This step lets you define exact matches or patterns.
- Apply any additional transformations needed, such as case sensitivity or partial matches.
- Generate your results by previewing the modified data and running your automated flow. Once configured, this process will update automatically.
See Parabola in actionHow to use Shopify with Parabola
Parabola seamlessly integrates with Shopify to help you automate your data transformation processes without any coding knowledge required.
- Direct integration with your Shopify store for real-time data access
- Automated data synchronization to keep your information current
- Visual workflow builder for creating custom data transformations
- Ability to combine Shopify data with other data sources
- Scheduled runs to keep your data consistently updated
Retrieving data from Shopify
Connecting your Shopify store to Parabola is straightforward using the Pull from Shopify step. This integration allows you to access various data types from your store, including orders, products, customers, and inventory information.
Key features
- Direct API connection to your Shopify store
- Multiple data type selection options
- Customizable date ranges for data retrieval
- Automatic pagination handling
- Real-time data refresh capabilities
How to use
- Add the Pull from Shopify step to your Flow
- Connect your Shopify account to Parabola
- Select the desired data type (orders, products, etc.)
- Configure any additional parameters or filters
- Run the step to retrieve your data
How to find and replace with Parabola
The find and replace functionality in Parabola allows you to make systematic changes to your data using simple rules. This powerful feature can transform your data according to your specific needs without requiring complex formulas or coding.
Key features
- Multiple find and replace rules in a single step
- Case-sensitive matching options
- Bulk operations across multiple columns
- Preview of changes before applying
- Find and replace values in entire cells or values in any part of the cell
How to use
- Add the Find & replace step to the Canvas
- Select the columns to apply the rule to
- Create your first find and replace rule
- Add additional rules as needed
- Preview the results to confirm the changes
Practical use cases and examples
Standardizing product categories
When managing a large product catalog, categories might be inconsistently formatted. Use find and replace to standardize categories like changing "Women's Apparel" to "Womens Clothing" across all products to maintain consistency.
Updating pricing formats
Convert price formats from one currency notation to another by finding specific patterns and replacing them with the desired format. For example, changing "10.00 USD" to "$10.00" across all product listings.
Correcting vendor names
Fix inconsistencies in vendor names by creating rules to standardize variations like "Nike, Inc.", "NIKE", and "nike" to a single consistent format "Nike" throughout your product database.
Using Parabola's find and replace functionality with your Shopify data helps maintain data consistency and accuracy across your e-commerce operations. By automating these transformations, you can save time and reduce errors in your data management processes while ensuring your store's information remains professional and standardized.
