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What are APIs?
APIs, or Application Programming Interfaces, are a way for different software applications to communicate with each other. They provide a standardized way for one application to access data or functionality from another application. APIs are commonly used to retrieve data from external sources, such as weather forecasts, stock prices, or social media activity.
Why would you want to use AI to automatically standardize your API data?
Standardizing API data is important because the data returned from different APIs can be in different formats, with varying data types, naming conventions, and levels of completeness. This can make it challenging to work with the data and integrate it into your own systems. By using AI to automatically standardize your API data, you can save time, reduce errors, and ensure that your data is consistent and usable.
Some key benefits of using AI to standardize your API data include:
- Improved data quality and consistency
- Reduced manual effort and time spent on data cleaning
- Ability to work with a wider range of data sources
- Scalability to handle large volumes of data
Explore and learn more about Parabola
Use Parabola to bring your disparate data and documents together, then tackle your most complex processes with ease
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Clean, standardize, and manipulate your data and make it actionable
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Pull from API
Standardize with AI
How to use APIs with Parabola
Parabola's API integration capabilities enable you to connect with virtually any external data source or service.
- Flexible configuration options for different API endpoints
- Support for various authentication methods
- Built-in error handling and response parsing
Retrieving data from an API
The Pull from API step in Parabola enables users to connect to virtually any API endpoint and retrieve data in real-time. This step handles authentication, request formatting, and response parsing automatically, making it accessible to users regardless of their technical expertise.
Key features
- Support for multiple authentication methods
- Automatic JSON parsing
- Custom header configuration
- Rate limiting protection
- Error handling and retry logic
How to use
- Add the Pull from API step to your Flow
- Enter the API endpoint URL
- Configure authentication settings
- Set up any required parameters, headers, pagination, and rate limiting settings
- Test the connection and preview data
Applying AI to standardize your data
Once you have imported your data into Parabola, you can use the Standardize with AI step to automatically clean and standardize it. This step leverages large language models to identify and correct inconsistencies, typos, and other data quality issues.
Key features
- Automatically standardizes values similar to those that you explicitly specify
- Add additional fine tuning to improve results from the model
- Supports a wide range of data types and formats
How to use
- Drag the Standardize with AI step onto your Flow's canvas, after you pull your data
- Specify whether you'd like to standardize values within a column or column names
- Define the value(s) you'd like to specify, including example values
- Click "Update results" to apply the AI-powered standardization to your data.
- Review and refine the standardization results as needed
Practical use cases and examples
Standardizing product data from multiple suppliers
Many businesses work with multiple suppliers, each of which may provide product data in different formats. By using Parabola to pull in this data and then standardize it with the Standardize with AI step, you can create a consistent, high-quality product catalog that can be used across your organization.
Cleaning and enriching customer data
Customer data can often be messy, with inconsistent formatting, missing information, and other quality issues. By using Parabola to pull in customer data from various sources and then standardize it with the Standardize with AI step, you can create a clean, enriched customer database that can be used for marketing, sales, and customer service.
Normalizing financial data
Financial data can be particularly challenging to work with, as it often includes a mix of currencies, units, and other specialized formatting. By using Parabola to pull in financial data from various sources and then standardize it with the Standardize with AI step, you can create a consistent, normalized dataset that can be used for financial analysis and reporting.
In conclusion, using Parabola and the Standardize with AI step can be a powerful way to automatically clean and standardize your API data, saving you time and improving the quality and consistency of your data. By leveraging the flexibility and automation capabilities of Parabola, you can build custom data workflows that meet your specific needs and requirements.
