An Introduction to Change Data Capture (CDC) Practices and Modern Approaches

This article is an introduction to Change Data Capture (CDC) practice rather than an in‑depth discussion of a specific tool.

Assume we are building a simple web application. In most cases such projects start with a minimal data schema; relational databases like MySQL or PostgreSQL are sufficient to store and manage the data that users query, update, delete, and correct, covering use cases such as CRM, ERP, banking, billing, or POS terminals.

However, the information stored in the database often interests many third‑party systems, typically analytical systems. Enterprises need to understand the state of applications or entities—accounts, deposits, manufacturing, HR, etc.—stored in the system. Data plays a crucial role in almost every business operation, so companies regularly generate reports reflecting key metrics needed for further management decisions.

Report and analytical queries are usually resource‑intensive; they may take hours to complete, severely impacting the performance of the systems from which data is retrieved. They also put a heavy load on the network, and decisions based on this data are delayed because the data is refreshed only nightly.

If a system has a clear low‑load window (e.g., nighttime) sufficient to offload all necessary data without affecting primary activities, direct queries to the RDBMS may be acceptable. But without such a window, or when the window is insufficient, what can be done?

Here the CDC process comes to the rescue. As the name suggests, Change Data Capture only captures changes in the data, which is one ETL pattern for data replication. It provides a mechanism to track changes in the source database and apply them to a target database or data warehouse, allowing all types of analysis and reporting without affecting the source database’s performance.

Thus, users can operate on the original system without performance degradation, while management can obtain up‑to‑date reports needed for decision‑making.

CDC

Therefore, the essence of CDC is to provide historical change information for user tables by capturing DML changes (INSERT/UPDATE/DELETE) and the changed data itself. CDC extracts these changes in a form that can be replicated upstream; this data is often referred to as a “delta”.

You can think of CDC as a mechanism that continuously monitors changes in the original data system, extracts them, and distributes them to upstream systems. Change Data Capture achieves near‑real‑time incremental data loading, eliminating the need for batch loads.

So how does CDC solve the problems we mentioned?

Because you are not running huge queries regularly, your load is not at peak levels; therefore you must ensure timely transmission of the data you need rather than sending all data at once, which would overload the network. By sending only incremental changes, you keep the system responsive and provide up‑to‑date information for real‑time business decisions.

CDC is essentially a special process in the database. Each time an event occurs in the database, a simple example SQL procedure is executed.

Thus, we need a simple table to track all changes, creating an object for each change that can be used downstream.

Extract Incremental

In analytics, CRM, MDM hubs, disaster recovery, extracting the “delta” of transactions is an architectural task; when parallel operations occur, data migration from one system to another often involves such tasks.

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Earlier, incremental data helped us understand the entire list of updated issues, but it could also lead to data loss if not handled properly.

To ensure no data is missed, engineers tried row‑level control, achieving similar results—functional but resource‑intensive.

All problems are solved simply by using CDC.

It is a special process in the database; each time an event occurs, a simple example SQL procedure runs.

Therefore, we need a simple table to track all changes, creating an object for each change that can be used downstream.

Production‑Ready CDC Systems

To build a production‑ready CDC system, besides extraction, we must consider:

Changes must be applied in order; otherwise the system may end up in inconsistent states.

Delivery guarantees are required; CDC must deliver at least once, and duplicate events must be handled to avoid state divergence.

Message transformation must be simple, supporting different data formats across systems.

All messages are reported—sources emit change events, listeners consume them, and the system propagates updates to target objects.

This solution offers many benefits, such as scalability. The subscription model allows primary sources to push more updates to target systems, scaling with the number of consumers, and enabling real‑time data handling.

Another benefit is that the two systems are now connected; if the source changes its database or moves a dataset, the target does not need to be altered, as long as the source continues to emit messages in the same format.

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