Change Data Capture (CDC)

Last updated Apr 16, 2024

• Primary Purpose: It enables capturing and streaming changes made to the database. It’s used for integrating databases with other systems, such as replicating data to a data warehouse or syncing it with a cache or a search index.
• Mechanism: CDC can be implemented using various techniques, including database triggers and polling, or by tapping into the database’s replication capabilities, such as using the WAL in PostgreSQL. It captures the “what” of the change (insert, update, delete) and the “how” (the actual data change).
• Usage in Databases: It’s not a core database functionality for ensuring consistency or durability but is essential for modern data architectures that require real-time data integration and streaming.

See a practical example of Change Data Capture (CDC) - Real-Estate Project.

Connection to Write-Ahead Logging (WAL)
In essence, WAL is an internal mechanism used by databases to guarantee consistency and durability, especially for recovery purposes. CDC, on the other hand, is an external mechanism used for data integration and replication.

They might intersect in the sense that CDC can utilize WAL (as in the case of PostgreSQL’s logical decoding feature) to capture changes, but their core purposes and implementations are different.

# CDC with Postgres

See also A Guide to Logical Replication and CDC in PostgreSQL | Airbyte

For implementing CDC in PostgreSQL, you would typically use a replication slot along with a logical decoding output plugin. There are various open-source and commercial tools available that leverage this feature to provide robust CDC capabilities, such as Debezium, pglogical, and others.

These tools can stream database changes in real-time and integrate with various data pipelines and ETL processes, making them invaluable in modern data architectures, especially in scenarios involving real-time data warehousing, analytics, and microservices.

Usually, organizations will have a relational database like Postgres to deal with transactional workloads and a separate data warehouse where the data can be subjected to analytics and consolidated for reporting purposes. This means the huge volumes of data pouring into the Postgres DBs need to be delivered to the data repository along with data changes as they happen.

Connecting PostgreSQL to other data stores often involves using data pipelines that rely on time-based batch processing (think of hourly or daily batch sync), so it is a challenge to obtain data for time-critical reporting

For modern enterprises that routinely deal with huge petabytes of data, the batch processing approach can prove problematic, impact source systems and lead to errors and inaccurate results. It may fail to capture up-to-date data, causing synchronization issues that hinder accurate data-driven decision making. This is where  Postgres CDC can help – to sync data between databases either continually, or at prescribed intervals. Change Data Capture (CDC) is a modern approach that can be applied in the Postgres database for achieving continuous, accurate and often real-time data sync.  Postgres to SQL Server with BryteFlow

CDC is a way to track and use data that has changed in a database. PostgreSQL has built-in CDC functionality. The choice of method depends on your application’s requirements and how much impact you can tolerate on performance. Let us discuss setting up Change Data Capture (CDC) in PostgreSQL using six different methods.

• CDC with Write-Ahead Logging (WAL)
• CDC in Postgres with Triggers
• CDC in Postgres with Queries or Timestamp Column
• CDC in Postgres with Logical Replication
• CDC in Postgres with Transactional Logs
• CDC in Postgres with Table Differencing

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Created 2022-08-16