You need to ensure that all changes to DB1 are stored in DW1.The solution must capture the new value and the existing value and store each value as a new record.

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 09:10:41 Viewed : 24

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 capture the new value and the existing value and store each value as a new record.

What should you include in the solution?

Ans: Change data capture

Change data capture captures every change to the data and presents the new values as a new row in the tables.

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To ensure that all changes to DB1 are captured and stored in DW1, including both the new value and the existing value as separate records, you can implement a process known as Change Data Capture (CDC). CDC is a technique used to track and capture changes made to a database over time. 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 Changed Data:

    • After enabling CDC, DB1 will start capturing changes to the specified tables. It maintains change tables (CDC tables) that store the details of insert, update, and delete operations. These tables include columns for the new value, the old value (before the change), and metadata like the operation type and transaction ID.
  3. Implement Data Flow to DW1:

    • Create a data flow or data integration process to move the captured change data from DB1 to DW1. This process can be implemented using various tools and technologies depending on your specific requirements and preferences. You may consider using tools like Azure Data Factory, Azure Databricks, or SQL Server Integration Services (SSIS) for this data movement.
  4. Transform and Store Data in DW1:

    • In DW1, design the data schema to accommodate the change data from DB1. You will need tables that mirror the structure of the CDC tables in DB1. For each change captured in DB1, insert corresponding records into the appropriate DW1 table, including the new and old values, operation type, and other relevant metadata.
  5. Scheduled Data Movement and Transformation:

    • 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.
  6. 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, you can create a solution that captures changes in DB1 and stores both the new and existing values as separate records in DW1, allowing you to track and analyze historical changes in your data warehouse.

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