How to Update SQL Records in Azure Data Factory Based on Conditional Logic

A guide for beginners on updating SQL records in Azure Data Factory using conditional logic to ensure only relevant records are modified.
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This video is based on the question https://stackoverflow.com/q/74550833/ asked by the user 'dEL' ( https://stackoverflow.com/u/4505544/ ) and on the answer https://stackoverflow.com/a/74556097/ provided by the user 'Mark Kromer MSFT' ( https://stackoverflow.com/u/7350788/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Updating SQL Records in Azure Data Factory: A Beginner's Guide

As a beginner in Azure Data Factory (ADF), navigating the complexities of data updates can be a challenge. One common scenario involves updating records in a SQL table based on certain conditions derived from a source file – in this case, an Excel file. If you're looking to understand how to effectively implement this in ADF, you've come to the right place!

In this guide, we'll break down a specific requirement: updating a SQL table only if a certain condition is met involving two columns: FileAccountID, DBLegacyID, and DBAccountID. Let's dive in and explore how to achieve this effectively.

The Problem Statement

You want to perform the following operation within your Azure Data Factory’s Data Flow activity:

Source: An Excel file containing account information.

Sink: A SQL table where you want to update records based on specific conditions.

Condition for Update: Only update the sink if the following condition is satisfied:

Condition: if(FileAccountID == DBLegacyID && FileAccountID != DBAccountID)

Given this requirement, how do we implement it efficiently in Azure Data Factory?

The Solution

To fulfill the requirement of conditional updates in Azure Data Factory, we need to utilize the Alter Row transformation. This powerful feature allows us to define rules for how rows in our sink should be modified based on conditions we specify.

Step-by-Step Guide

Here’s how you can set this up using an Alter Row transformation:

Add an Alter Row Transformation:

Once you have your Data Flow set up with both the source (Excel file) and the sink (SQL table), the next step is to add an Alter Row transformation to your flow.

Configure the Update Rule:

Inside the Alter Row transformation, you will need to specify an update rule. This rule will be based on the condition you defined.

Here’s what you should do:

Click on the “Add a new rule” button.

Set the Update property action for the row.

Add an expression representing your condition:

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

Make sure this expression correctly references the fields in your data schema.

Finish Your Data Flow:

After you have configured the Alter Row transformation, connect it to your sink (SQL table) ensuring the correct mapping between source and destination columns.

Validate and debug your Data Flow to ensure that all steps return expected results.

Test the Implementation:

Run your Data Factory pipeline to test the entire process.

Monitor the output in the SQL table to confirm that updates only occur when your condition is satisfied.

Conclusion

Using the Alter Row transformation in Azure Data Factory simplifies the process of updating records conditionally. By following the steps outlined, you can ensure that your SQL table is only updated when the specific requirements are met, preventing unwanted data modifications.

As you continue your journey with Azure Data Factory, remember that every problem has a solution, and breaking down complex tasks into smaller steps can lead to great success. Happy data processing!

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