You need to ensure that all changes to DB1 are stored in DW1. The solution must meet the following requirements:

Category : Microsoft Azure Data Engineering | Sub Category : Practice Assessment for Exam DP-203 - Data Engineering on Microsoft Azure | By Prasad Bonam Last updated: 2023-09-10 03:45:05 Viewed : 294

You have a database named DB1 and a data warehouse named DW1.

You need to ensure that all changes to DB1 are stored in DW1. The solution must meet the following requirements:

• Identify that a row has changed, but not the final value of the row.
• Minimize the performance impact on the source system.

What should you include in the solution?

Ans: change data capture

Change tracking captures the fact that a row was changed without tracking the data that was changed. Change tracking requires fewer server resources than change data capture.

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To ensure that all changes to DB1 are stored in DW1, including identifying that a row has changed without capturing the final value of the row while minimizing the performance impact on the source system, you can implement Change Data Capture (CDC) and use the "net changes" approach. Here is what you should include in the solution:

1. Enable CDC on DB1:

   • First, you need to enable CDC on the source database, DB1. You can enable CDC at the database level and specify which tables you want to track for changes. SQL Server provides built-in CDC functionality for this purpose. You enable CDC for each table you want to monitor.

2. Capture Changes to CDC Tables:

   • After enabling CDC, DB1 will start capturing changes to the specified tables in CDC tables. These change tables store metadata about the changes, such as the operation type (insert, update, delete) and the transaction ID.

3. Identify Row-Level Changes:

   • Implement a mechanism in your ETL process to identify row-level changes. In CDC, the change tables store information about which columns have changed, but not the final value of the row. To identify changes, you need to compare the current values in the source table with the previous values stored in the CDC tables.

4. Transfer Changes to DW1:

   • Create a data flow or ETL process to transfer the identified changes from the CDC tables in DB1 to DW1. This process should be designed to minimize the impact on the source system. It can be implemented using Azure Data Factory, Azure Databricks, or SQL Server Integration Services (SSIS).

5. Store Changes in DW1:

   • In DW1, design tables to store the identified changes from DB1. You will need tables that mirror the structure of the source tables in DB1, but with additional columns to store the change details. These tables should include columns for the primary key, operation type, column name, and the new and old values.

6. Scheduled Data Movement:

   • Schedule the data movement and transformation process to run periodically to keep DW1 synchronized with changes in DB1. The frequency of data movement depends on your specific requirements, such as near real-time, hourly, or daily updates.

7. Data Validation and Monitoring:

   • Implement data validation and monitoring to ensure the accuracy and completeness of data movement from DB1 to DW1. You may need to perform validation checks and set up alerts in case of any issues.

By following these steps and implementing CDC with a focus on capturing row-level changes, you can track changes in DB1 and store them in DW1 while minimizing the performance impact on the source system. This approach allows you to maintain a historical record of changes without capturing the final value of the row.