Change Data Capture 101: Keeping Systems in Sync in Real Time
This blog explains Change Data Capture (CDC) – a technique for tracking and streaming database changes in real time – and covers its working.
Imagine you’re running an e-commerce app with a bunch of connected services – a main database, a search index, a caching layer, maybe a analytics dashboard.
A customer updates their profile or places a new order, and instantly all these systems reflect that change.
How is that possible?
The answer is often Change Data Capture (CDC) working behind the scenes.
In today’s real-time world, users and businesses expect data to be up-to-date everywhere.
Traditional batch updates (like nightly ETL jobs) just won’t cut it – they introduce latency and leave you with stale data.
CDC comes to the rescue by capturing changes as they happen and delivering them immediately wherever they need to go.
It’s the secret factor that keeps distributed systems in sync without waiting for the next batch cycle.
What is Change Data Capture (CDC)?
At its core, Change Data Capture is a design pattern (and a set of tools/techniques) used to track and record every change in a database – inserts, updates, deletes – and then notify or stream those changes to other systems in real time.
Think of CDC as a vigilant messenger sitting by your database, watching for any data update, and instantly shouting, “Hey, this row changed!” to any other service that cares.
By doing so, CDC ensures that if one system’s data is modified, all the other connected systems (like downstream microservices, analytics databases, or caches) get the news and update themselves accordingly.
This helps maintain data consistency across different components of a software architecture without heavy, slow bulk data transfers.
In simpler terms, CDC answers the question: “How do we automatically tell System B that System A’s data just changed?” – and it does so continuously and reliably.
How Does CDC Work?
So, how do we capture data changes as they happen? The general process of CDC involves a few key steps:
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Monitor the Source: The CDC system monitors the primary database for any new transactions or modifications. This can be done by different means – for example, adding database triggers that fire on updates, or more advanced methods like reading the database’s transaction log (a record of all changes). There are even specialized CDC tools that plug into your database for this purpose.
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Capture the Change: When a relevant change (insert/update/delete) occurs, CDC captures the details of that event. This includes what data was changed and how. For instance, if an order status changed from “pending” to “shipped”, the CDC capture would record the order ID and the new status (and sometimes the old status and timestamp as well).
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Deliver the Update: After capturing the change, CDC delivers this information to a downstream system or pipeline. Often, the change events are sent to a message broker or stream (like a Kafka topic, for example) that other services or data pipelines subscribe to. These consumers then apply the changes to their own database, index, or cache. In short, CDC turns database changes into events that can travel across your architecture.
Behind the scenes, there are a few common ways to implement CDC.
Some systems use a simple approach like a “LastUpdated” timestamp on rows and periodically query for new changes.
Others use trigger-based CDC, where database triggers record changes into a separate table as they happen.
Modern, scalable setups often prefer log-based CDC, which reads the database’s commit log (binlog/WAL) to grab changes after they happen, with minimal performance impact on the primary database.
Tools like Debezium (an open-source CDC platform) combined with Kafka are popular solutions to stream changes from databases like MySQL or PostgreSQL in real time.
The exact method can vary, but the goal is the same: never miss a change, and propagate it ASAP.
Why Use CDC?
Now that we know what CDC is and how it works, you might wonder why it’s such a big deal.
Here are some key benefits and use cases for Change Data Capture in modern systems:
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Real-Time Data Synchronization: CDC ensures all connected systems have the latest data almost instantly. Your analytics database, search index, and caches get updates continuously, not hours later. This is crucial for scenarios like financial services or online gaming where even a few minutes of data lag can cause problems.
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Event-Driven Architecture: By turning database changes into events, CDC enables event-driven microservices. For example, instead of a monolithic app calling multiple services to tell them “I changed something”, each service can just listen for change events on a message queue. This decouples services and makes the system more scalable and fault-tolerant. It’s like publish/subscribe for your database updates – a core idea in system design for modern distributed applications.
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Reduced Dependency on Batch ETL: Traditionally, companies used batch ETL processes to copy data from one store to another (say from a production DB to a data warehouse) maybe once a day. CDC provides a smarter alternative by streaming only the changes, which reduces latency and avoids the heavy load of bulk transfers. Your reporting and machine learning systems can work with up-to-the-minute data without hammering the primary database.
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Up-to-Date Analytics & Reporting: With CDC, business intelligence dashboards, analytics platforms, or even real-time monitoring systems always operate on the freshest data. For instance, an e-commerce company can replicate its orders database to an analytics database continuously. Analysts and dashboards see new orders almost as soon as they happen, enabling quicker insights and decisions. No more waiting for “today’s data” to be available tomorrow.
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Cache Invalidation and Read Models: If your application uses caching or maintains derived read models (like an Elasticsearch index for searching), CDC can automatically handle updates for you. When underlying data changes, CDC events can trigger cache invalidations or update the search index document that changed. This means users are less likely to see stale information, and you don’t need clunky polling mechanisms to refresh caches. It’s all event-driven and automatic.
Of course, adopting CDC isn’t without challenges.
There’s added complexity in setting up the pipelines and ensuring everything stays reliable (ordering of events, error handling, etc.).
High-throughput systems need a CDC solution that can keep up with the volume of changes.
And there’s a need for careful handling of schema changes or data mismatches across systems.
However, with the robust tools and best practices available today, CDC has become a proven strategy to tackle real-time data sync problems.
Conclusion
In a world where immediacy is expected, Change Data Capture has emerged as a game-changer for data management. It keeps your data ecosystem agile, consistent, and responsive by sharing changes the moment they occur.
For developers and architects, understanding CDC is increasingly important – not just as a buzzword, but as a fundamental concept in building real-time, scalable systems.
If you’re preparing for system design interviews or aiming to level up your software design skills, consider diving deeper into patterns like CDC and other data streaming techniques.
By mastering approaches like Change Data Capture, you’ll be well on your way to designing systems that can handle the demands of today’s data-driven applications. Happy learning!
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