In this article, we will discuss the concept of incremental data loading using Change Tracking in SQL Server. Incremental data loading is a process of updating only the changed or new data in a target database, rather than reloading the entire dataset. This can significantly improve the efficiency and performance of data integration processes.

Change Tracking is a feature in SQL Server that tracks the changes made to a table. It records the primary key values of the changed rows, along with the type of change (insert, update, or delete). By leveraging Change Tracking, we can easily identify the modified data and synchronize it with the target database.

Let’s consider an example where we have three related tables: Faculty, Department, and Student. We want to perform incremental data loading from an on-premise SQL Server to an Azure SQL database using Azure Data Factory (ADF) and Change Tracking.

Here are the steps involved in the incremental data loading process:

1. Create the tables in the on-premise SQL Server and Azure SQL database with the same structure.
2. Enable Change Tracking on the on-premise SQL tables.
3. Create a table to maintain the change tracking version information.
4. Populate the change tracking version information for the three tables.
5. Insert initial data into the source tables.
6. Create staging tables in the Azure SQL database with additional columns for change tracking information.
7. Copy data from the source tables to the staging tables using ADF.
8. Create a stored procedure to handle the update, insert, and delete operations on the target tables.
9. Execute the stored procedure to update the target tables based on the changes in the staging tables.
10. Update the change tracking version information in the tracking table.

By following these steps, we can achieve incremental data loading from the on-premise SQL Server to the Azure SQL database using Change Tracking and ADF. This approach ensures that only the modified data is synchronized, reducing the processing time and network bandwidth.

Incremental data loading is particularly useful in scenarios where the source data is frequently updated, and we need to keep the target database up-to-date without reloading the entire dataset. It is commonly used in data warehousing, data integration, and data synchronization processes.

Overall, incremental data loading through ADF using Change Tracking in SQL Server is a powerful technique for efficient and reliable data integration. It allows us to keep the target database synchronized with the source database, minimizing the data transfer and processing overhead.

Thank you for reading this article. We hope you found it informative and helpful in understanding the concept of incremental data loading using Change Tracking in SQL Server.