Leveraging Azure Data Factory to Retrieve Data from a REST API
Microsoft Azure Data Factory is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation. One common use case is retrieving data from a REST API and inserting it into an Azure database table. This blog post will guide you through this process, providing practical examples and code snippets.
Azure Data Factory and REST APIs
Azure Data Factory offers a generic HTTP connector and a specific REST connector. These connectors allow you to retrieve data from HTTP endpoints using GET or POST methods. Here’s an example of how you can set up an HTTP Linked Service:
"url" : "https://en.wikipedia.org/wiki/"
In this example, we create an HTTP Linked Service that connects to a specific URL. The
authenticationType is set to “Anonymous”, meaning no authentication is required to access the endpoint.
Using Azure Data Factory for Batch Processing
Azure Data Factory is particularly useful if you want to run batches on a schedule and have a single place for monitoring and management. For instance, you can use Azure Data Factory to load data from an HTTP endpoint to a blob. Then, the blob can be consumed for other processing, like inserting it into your table.
To achieve this, you would create a pipeline that contains your custom loader. Then, you would create an activity with the blob as the input dataset and the table as the output dataset.
Support for REST as a Data Source
As of January 2019, Azure Data Factory now supports REST as a data source, including pagination. This means you can retrieve data from REST APIs in a more efficient and streamlined manner, especially when dealing with large amounts of data that require multiple requests to retrieve.
Azure Data Factory provides a powerful and flexible way to retrieve data from REST APIs and insert it into Azure database tables. Whether you’re dealing with simple or complex data workflows, Azure Data Factory has the tools and features to meet your needs.
Remember, the key to mastering Azure Data Factory is practice and exploration. Don’t hesitate to try out these examples and experiment with different configurations to suit your specific requirements. Happy data processing!