Understanding ADF Pipelines and Data Flows for SQL Deletes

Azure Data Factory (ADF) is a cloud-based data integration service that enables you to create, schedule, and manage workflows that move data between various sources, including on-premises data stores like SQL Server, and Azure-based destinations. In this article, we’ll delve into the world of ADF Pipelines and Data Flows, exploring how to transfer SQL deletes to destination staging tables.

Introduction to ADF Pipelines

An ADF Pipeline is a collection of tasks that are executed in sequence to transform, move, and govern data in various destinations. The pipeline workflow can be customized using various connectors, including databases, file systems, and cloud-based services.

When creating an ADF Pipeline, you have several options for processing SQL Server data, including:

1. SQL Query Transformation: This transformation enables you to execute a custom SQL query on the source data.
2. Change Data Capture (CDC): This feature captures changes made to the data in real-time and applies them to the target environment.

However, when dealing with delete operations, ADF Pipelines don’t directly support transferring deleted records to staging tables for comparison and replication.

Using Data Flows in ADF

Data Flows is a new feature introduced in Azure Data Factory, which enables you to create scalable data pipelines that can handle large volumes of data. Data Flows offers several benefits over traditional ADF Pipelines, including:

1. Faster Pipeline Execution: Data Flows are executed in parallel, reducing the overall processing time.
2. Scalability: Data Flows can be scaled up or down to meet changing data processing requirements.

To transfer SQL deletes to destination staging tables using Data Flows, you’ll need to create a new pipeline and add a Data Flow activity.

Creating a Data Flow Activity

To create a Data Flow activity in ADF, follow these steps:

1. Navigate to your Azure Data Factory workspace and click on the " authoring" tab.
2. Click on “Create pipeline” and select the “Data Flow” activity from the list of available activities.
3. Name your data flow activity (e.g., “Delete Records Staging”) and choose the SQL Server source connector.

Configuring the Data Flow Activity

Once you’ve created the data flow activity, you’ll need to configure it for delete operations:

1. In the “Data Flow Editor,” click on the “Activities” tab and add a new transformation called “Exists.”
2. Configure the “Exists” transformation to check for the existence of records in the staging table.
3. Next, add an “Alter Row” transformation to set update/insert/upsert/delete policies based on the result of the “Exists” transformation.

Example Transformation Configuration

Here’s an example configuration for the “Exists” and “Alter Row” transformations:

## Exists Transformation

- Transform type: Exists
- Condition: Source.staging_table_id = Destination.main_table_id
- Join condition: staging_table_id

## Alter Row Transformation

- Transform type: Alter Row
- Policy:
  - Update policy: Set the staging table_id and delete policy to set the delete policy to "delete" when exists is false.
  - Insert policy: When exist is true, insert into main table with staging_table_id as a foreign key.
  - Upsert policy: When both conditions are met, update in main table with staging_table_id as a foreign key

Executing the Data Flow Activity

After configuring the data flow activity, you’ll need to execute it:

1. Click on the “authoring” tab and select the pipeline that contains your new data flow activity.
2. Click on the “Author & Monitor” button and then click on the “Run” button.

Comparing Records with Destination Main Tables

Once you’ve executed the data flow activity, you’ll have deleted records transferred to the staging table. To compare these records with the destination main tables, you can use additional transformations in your data flow activity:

1. Add a new transformation called “Merge Join.”
2. Configure the merge join transformation to match the records between the staging table and the main table.

Example Merge Join Transformation

Here’s an example configuration for the merge join transformation:

## Merge Join Transformation

- Transform type: Merge Join
- Condition: staging_table_id = main_table_id
- Join condition: staging_table_id

Replicating Records from Staging Table to Azure Destination

After comparing records with destination main tables, you’ll need to replicate the matching records from the staging table to an Azure destination:

1. Add a new transformation called “Azure SQL Database.”
2. Configure the Azure SQL Database transformation to write data to the target database.

Example Azure SQL Database Transformation

Here’s an example configuration for the Azure SQL Database transformation:

## Azure SQL Database Transformation

- Transform type: Azure SQL Database
- Connection string: Your Azure SQL Database connection string.
- Table name: main_table

Conclusion

In this article, we explored how to transfer SQL deletes to destination staging tables using ADF Pipelines and Data Flows. By leveraging the new features introduced in Data Flows, you can create scalable data pipelines that handle large volumes of data.

Best Practices

When working with ADF Pipelines and Data Flows, keep the following best practices in mind:

1. Monitor your pipeline performance: Use Azure Monitor to track your pipeline’s performance and optimize as needed.
2. Optimize data flow transformations: Use the “Performance” tab to identify slow-running transformations and optimize them for better performance.
3. Test your pipeline thoroughly: Test your pipeline with sample data before running it in production.

By following these best practices, you can create efficient and scalable ADF Pipelines that efficiently transfer SQL deletes to destination staging tables.

Last modified on 2025-02-02