Change Data Capture (CDC) in Databases: Techniques and Tools

When your application starts small, handling data changes is usually simple. A relational database like PostgreSQL or MySQL stores your transactions, and periodic batch updates keep everything synchronized. But as your application grows—more users, more transactions, and more data—this solution starts to break down. The more you scale, the harder it becomes to manage and synchronize real-time data efficiently. This is where Change Data Capture (CDC) comes in.

In this article, we’ll explore why CDC is essential for scaling your application, how it works, and some tools that can help you implement it effectively.

What Is Change Data Capture (CDC)?

Change Data Capture is a technique that continuously monitors and captures changes in a database. Instead of processing the entire dataset repeatedly, the CDC focuses only on what’s changed—whether it’s an update, insert, or delete. This approach reduces the processing load and ensures that downstream systems receive changes almost instantly.

A great example of how this technology is used is in ETL (Extract, Transform, Load) applications. These tools transfer only the updated data from SQL database tables to a data warehouse or data storage. Instead of creating a full copy of the source tables, ETL applications need a reliable stream of changes that can be applied to different formats in the target system. SQL Server’s change data capture (CDC) feature makes this possible by providing a structured stream of change data that can be easily used by ETL tools.

How CDC Works

CDC tracks changes in a database using different methods, such as:

• Log-based CDC: Databases maintain logs (e.g., PostgreSQL WAL logs, MySQL binlogs) of all transactions. CDC can read these logs to identify changes without directly querying the data tables, reducing the load on the database. It’s highly efficient and minimally intrusive.
• Trigger-based CDC: Triggers are database mechanisms that automatically execute predefined actions in response to certain events, such as data modifications. In the CDC, triggers can log changes to a separate table for later processing.
• Timestamp-based CDC: Each row in a table includes a timestamp indicating the last modification time (“LAST_UPDATED” or “DATE_MODIFIED” columns). CDC can identify changes by comparing these timestamps to determine which rows have been updated since the last check. While simple, this method may miss updates if timestamps aren’t configured properly.
• Difference-based CDC: Table rows are assigned version numbers that increment with each change. This type of CDC compares snapshots of the database to detect changes. It’s easy to implement but not suitable for real-time use cases.

Benefits of Implementing CDC

There are many ways you can use Change Data Capture (CDC) in your data integration strategy. For example, you might need to move data into a data warehouse or data lake, create a real-time replica of your database, or build a modern data architecture. CDC helps your organization get more value from your data by enabling faster integration and analysis while using fewer system resources. Here are some key benefits:

• Faster Data Updates: CDC replaces slow batch updates with real-time or incremental data loading, keeping your systems up to date without delays.
• Efficient and Low Impact: Log-based CDC minimizes the impact on your source database when capturing new data changes.
• Supports Critical Use Cases: Real-time CDC is perfect for database migrations without downtime, real-time analytics, fraud detection, and syncing data across different regions.
• Ideal for Multi-cloud Adoption: With more companies moving to the cloud for lower costs and greater flexibility, maintaining consistent and up-to-date data across on-premises and cloud environments is critical. CDC efficiently moves data over wide networks, making it a great choice for zero-downtime cloud migrations.
• Works with Stream Processing: CDC integrates seamlessly with tools like Apache Kafka or GlassFlow, enabling real-time stream processing.
• Continuous Data Replication: As businesses shift from monolithic systems to microservices, they often need to move data from a single source database to multiple destinations. CDC helps by keeping both the source and target systems synchronized during this transition, ensuring seamless data flow as microservices architectures are adopted.

Tools to Implement CDC

Several tools can help you implement CDC in your architecture. Here’s a look at some of the most popular options:

1. Debezium: An open-source CDC tool that integrates with popular databases like MySQL, PostgreSQL, MongoDB, and more. It uses log-based CDC to capture changes with minimal impact on the database. A common use case for Kafka Connect is the database CDC. By using Debezium CDC connectors, you can seamlessly connect Kafka to your database and stream data directly into Confluent with ease. However, this integration is more suitable for Java applications. If you are using pure Python in your application, it is not straightforward to connect the Python application to Debezium.
2. Cloud-based CDC services: AWS Database Migration Service (DMS) supports CDC for migrating databases to the cloud or syncing data between databases. It’s a good choice for cloud-native applications. Google Cloud Datastream is ****a serverless service for CDC and replication. It’s ideal for real-time data integration with Google Cloud services.
3. Streaming Databases: Streaming databases like Materialize or RisingWave are PostgreSQL compatible, and combine CDC with real-time SQL querying capabilities, allowing you to build applications that react to changes instantly.
4. Databases with real-time features: Some database vendors like Supabase, Google Firestore, Amazon DynamoDB, or Azure CosmosDB provide embedded CDC. These databases can detect changes and use trigger-based CDC. For example, Supabase is a modern open-source backend database platform that integrates CDC with PostgreSQL. It can send database changes via webhooks to event-driven pipelines like GlassFlow for further processing.

Combining CDC with Event-Driven Architecture

CDC becomes even more powerful when integrated into an Event-Driven Architecture (EDA). In this setup, changes captured by CDC are treated as events, triggering actions in real time. GlassFlow converts database changes into actionable events within its event-driven pipelines.

Example:

Let’s say you’re using Supabase as your backend database. When a customer updates their shipping address, Supabase’s CDC feature captures the change and sends it as a webhook event to GlassFlow. GlassFlow processes the event formats the data, and notifies the shipping service, so that the change is reflected across all subscribed services or apps instantly. This integration makes your AI applications react to events without delay, keeping your app dynamic and responsive.

Challenges of CDC at scale and how GlassFlow helps

While CDC is a powerful method for capturing and propagating database changes, implementing it at scale comes with several challenges:

1. Database Performance: Capturing changes continuously can add load to the source database, especially if the triggers-based CDC method is used. GlassFlow minimizes database load by processing changes asynchronously and offloading all transformation tasks to its serverless environment, ensuring that the data source remains performant even under heavy usage.
2. Data Duplication: Poorly designed pipelines may lead to duplicate events, which can affect data consumers. GlassFlow ensures data consistency by deduplicating events within its pipelines using built-in message broker and processing rules.
3. Complexity: Scaling CDC across multiple databases, regions, and data formats can quickly become overwhelming. GlassFlow simplifies the complexity by providing a unified platform for managing multiple CDC pipelines efficiently.

Conclusion

As your application scales, the ability to handle real-time data changes becomes critical. Change Data Capture (CDC) ensures your application stays synchronized and responsive, enabling your AI models and workflows to deliver accurate and timely results. Tools like Debezium, Supabase, and streaming databases like Materialize make implementing CDC easier, reducing the complexity of scaling your architecture. By combining CDC with event-driven architectures and modern tools, you can future-proof your application, ensuring it remains efficient and competitive as data volumes grow. GlassFlow helps to build even-driven pipelines without infrastructure complexities.