How to Parse Collection-Like Input in a SQL Procedure Called from Data Factory

Learn how to efficiently handle collection-like inputs in Azure Data Factory to ensure smooth operations with SQL procedures.
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How to Parse Collection-Like Input in a SQL Procedure Called from Data Factory

In the world of data integration and management, handling various data types can often lead to unexpected challenges. One common hurdle is dealing with collections when passing data from Azure Data Factory (ADF) to SQL procedures. If you've encountered issues with parsing collection-like inputs, you're not alone.

The Problem: Passing Collections in Data Factory

Imagine you're working on an Azure Data Factory pipeline, and you need to pass a collection of filenames to a stored procedure for processing. The collection comes from a lookup activity called EnumNewFiles, and is represented as:

[[See Video to Reveal this Text or Code Snippet]]

However, you notice that the representation of the object being stored in your database appears as System.Collections.Generic.List1[System.Object], which isn't the format you expected. Ideally, you want a JSON representation similar to:

[[See Video to Reveal this Text or Code Snippet]]

Unfortunately, ADF doesn’t come with built-in functions to automatically serialize this collection into JSON.

The Solution: Serializing the Collection to JSON

To solve this problem, you need to convert the collection into a JSON string that SQL Server can work with. By using a simple expression within your ADF pipeline, you can achieve this serialization effectively.

Steps to Serialize the Collection

Use the ADF Expression: You can use the following expression to convert the child items to a string format:

[[See Video to Reveal this Text or Code Snippet]]

String Conversion: Alternatively, if needed, you can explicitly convert the activity's output to a string by utilizing this syntax:

[[See Video to Reveal this Text or Code Snippet]]

Why This Works

JSON Compatibility: By converting the collection to a JSON string, you ensure that SQL Server understands the format of the data that’s being passed. JSON is a widely accepted data interchange format, making it easier for various systems to communicate with one another.

Flexibility: Once the collection is serialized, you can retrieve and manipulate the data as needed within your stored procedure, allowing for more flexible data handling.

Optional: Validate Your JSON Format

Before passing the JSON string to SQL Server, it's good practice to validate the format. Ensure that your JSON string adheres to correct serialization standards to avoid runtime errors in your SQL procedures.

Conclusion

Managing collection-like inputs in Azure Data Factory can be a bit daunting, especially when interfacing with SQL Server. However, by taking advantage of ADF's expression capabilities and converting collections into manageable JSON strings, you set yourself up for success.

With the steps outlined in this guide, you'll be able to effectively parse collection-like inputs in your SQL procedures, improving the overall efficiency of your data pipelines.

By navigating these challenges thoughtfully, you can ensure that your data processing tasks run smoothly and without unnecessary hiccups.