Change Data Capture and When to Use It

Change data capture (CDC) can be valuable for enterprises of all sizes. Understanding how this process works and why it is beneficial can encourage your organization to implement CDC and reap the many advantages.

What Is Change Data Capture?

Change data capture is a software process that identifies and tracks changes in a database so data is always accurate. CDC can provide these updates in real-time or near-real-time by moving and processing data the moment new events occur.

CDC is a method commonly used with Extract, Transform, Load (ETL). ETL extracts data from various sources and delivers it to a database, data warehouse, or data lake. CDC can be the process used for the extraction phase.

Methods of Change Data Capture

There are three methods of applying change data capture in a given system:

• Script-based: Code CDC at the application level by altering the existing rows or metadata in a given source. In this method, you typically add fields to existing rows to identify which data has been changed.
• Query-based: With this approach, you must query the data in the source to identify changes. You often need a timestamp in the data to do this effectively, which is invasive to your source system.
• Trigger-based: Change data capture will run in response to a given trigger or event. This option can be efficient, but too many triggers can overload your system.
• Log-based: CDC examines the database's transaction log to reveal source system changes. After examining these logs, CDC will replicate all source changes in the database.

The Benefits of Change Data Capture

CDC supports enterprise success in various ways. This method:

• Improves decision-making: With CDC keeping your data up to date at all times, you can trust the data to be accurate when your enterprise makes a decision. This high level of accuracy in decision-making can lead to significant impacts on your bottom line.
• Minimizes disruptions: Incremental updates with CDC reduce the need for batch load updates that disrupt entire productions. Incremental source updates also support more efficient scaling and more effective high-volume data transfers.
• Reduces costs in the WAN: Cloud data transfers can be costly when the volume of data is larger than the capabilities of the Wide Area Network (WAN). Incremental changes are manageable for the WAN and help enterprises control costs related to these transfers.

When to Use Change Data Capture

Change data capture is valuable in any data integration platform. With CDC's ability to accelerate reporting and connect different database systems, many types of businesses can benefit from the process.

One use case for CDC is connecting software tools to in-house database systems that would otherwise be incompatible. With CDC supporting greater compatibility, enterprises have more flexibility when choosing business applications. Organizations are no longer limited and can deploy applications that align with their goals.

Other use cases for change data capture include:

• Creating a single source of truth for Business Intelligence tools
• Federating actionable data for internal and external teams
• Unifying reporting and insights to support Machine Learning models

How to Implement a Change Data Capture System

You can apply any of the methods above to implement a change data capture system, but you may need a framework before you can use these methods. To implement CDC, start with the basic concepts of ETL:

• Extract: Extract your data from any range of sources and place it in a database, data warehouse, or data lake.
• Transform: Apply your business rules and regulations to standardize and prevent duplication. Verify all data assets and sort them into given fields.
• Load: Load your transformed data into its storage location for access.

Once these steps are complete, you can implement your preferred method of CDC into the extraction phase.

Get Started With Change Data Capture With RightData

RightData supports efficiency and accuracy in data management with two tools — Dextrus and RDt. With Dextrus, you can implement query-based or log-based CDC to update data in increments and improve the accuracy of data-based decisions. RDt allows you to perform ETL testing to ensure the validity of your entire system.

Explore our solutions today and request a demo for more information.