Change Data Capture: Overview, Why, and Best Practices

Today’s businesses, especially those that prioritize digital transformation, are in dire need of real-time data. Traditional weekly and monthly batch processing can no longer meet demand. However, it is not easy to obtain real-time data from multiple sources and use it to automate processes and dynamically optimize decisions.

Recently, we encountered a challenge when re-architecting a customer's legacy system and splitting the monolithic architecture into microservices. We started making changes to the database and modernizing the system by module. At this stage, we need to ensure that both databases remain in sync, as different modules may require the same data - in other words, the old system requires data generated by the new system in the new database, and vice versa.

We researched Change Data Capture (CDC) technology to determine if it fit our needs. The article details the definition of CDC, the tools we tested, how they work and their advantages. At the same time, we shared some cases and suggestions to help other technicians choose the appropriate CDC tool in specific situations.

What is change data capture?

Data capture refers to the process of detecting and capturing changes in the source system and then delivering these changes to the target system in near real-time. These changes may include insert, delete, update operations, and DDL changes to the database structure.

How change data capture tools work

CDC tools implement their functions by monitoring data changes in the source system. Once a change is discovered, the CDC tool captures and records it in a designated location, such as a database or log file. The processed and transformed data is then loaded into a target system, such as a data warehouse or analytics platform.

There are many ways to capture database changes. Let’s take a look at some of them:

1. Timestamp/query based

In this method, we will maintain some audit columns similar to CREATED_AT, LAST_UPDATED or DATE_MODIFIED in the source and detect changes in these columns by querying the data in the source to capture any data changes . It should be noted that this method does not record deletion operations.

2. Trigger-based

A trigger is a function in the database that performs operations based on specific events. Although useful for capturing any change, including delete operations, it reduces database performance because each event requires multiple writes.

3. Log-based

The database contains a transaction log for recovery in the event of a crash, storing all events. With log-based CDC, new database transactions are read directly from the native log, which allows changes to be captured without scanning the source table and is therefore more efficient.

This approach is similar to event sourcing in event-driven architecture. Whenever the system state changes, we record it as an event. The recorded events can be replayed in the same order to reconstruct the system state at any time.

Why use CDC?

CDC is critical in many scenarios depending on the situation, application, architecture and business needs. Here are some of the ways the CDC helps with the engineering process:

• Real-time data availability: CDC tools capture changes in near real-time, ensuring the latest data is available for analysis, reporting, or further processing.
• Faster Decision Making: CDC helps reduce the delay between capture and data availability, enabling faster analysis and decision making.
• Efficient data integration: CDC tools help capture data from multiple operational sources and convert it into a common format in a single target database or data lake.
• Custom design of target database: CDC provides cross-functional benefits such as creating read-only search or query databases in CQRS systems, creating audit databases, or capturing data in data warehouses. It allows for decoupling non-functional and architectural requirements from the primary data store.
• Simplified data migration: In our case, CDC helps maintain data consistency between legacy and new databases during the modernization phase. This applies to various other data migration scenarios as well.

How to choose the right CDC tool?

There are several CDC tools on the market, such as Oracle Golden Gate, Debezium, IBM Infosphere, Striim, StreamSets and Qlik Replicate. These tools can be open source or paid. They typically support on-premises and cloud environments and can handle a variety of data sources. When choosing, consider the following:

• Compatibility with data sources: At a minimum, the tool you choose must be compatible with all data sources you want to capture changes to.
• Real-time data capture: Tools should capture changes in near real-time so that you can work with the latest data.
• Data conversion and integration: CDC tools should be able to handle data conversion from source to target data types.
• Price: CDC tools must be cost-effective for your use case. There are open source, paid and licensed products available.
• Ease of use and support: The tool should be easy to use for your team and provide adequate support, including comprehensive documentation and technical support.
• Other features: Depending on your needs, you may also want to check out other specific features, such as two-way synchronization between source and destination and cloud support.

As businesses become technology-driven, historical and current data will become a critical differentiator. Achieving accurate, timely, efficient and cost-effective change data capture will be an important part of any technology transformation initiative. When you face this situation, I hope this article can help you.

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