Change Data Capture (CDC) is a method used to track and capture changes in a database. With CDC in PostgreSQL, Guepard can replicate changes efficiently, making it ideal for maintaining synchronized lower environments, data analytics, and event-driven architectures. By capturing real-time changes, CDC enables event-driven applications to react dynamically to database updates, triggering workflows, notifications, or data processing pipelines as soon as changes occur. Guepard leverages CDC to provision lower environments quickly, ensuring they remain up-to-date with production data while maintaining isolation.

📌 Note: CDC methods vary in complexity and performance impact. Choose the right approach based on your use case and infrastructure constraints.

How CDC Works in PostgreSQL and Integration with Replication Tools

PostgreSQL supports CDC through logical replication, WAL (Write-Ahead Logging) streaming, trigger-based mechanisms, and polling-based CDC. Each method offers different advantages depending on use case requirements. Logical replication allows selective table-level replication, WAL streaming ensures efficient log-based changes, trigger-based CDC provides finer control over data change tracking, and polling-based CDC periodically queries tables to detect modifications. This enables databases to capture INSERT, UPDATE, DELETE events in real-time. Logical replication allows selective table-level replication, WAL streaming ensures efficient log-based changes, and trigger-based CDC provides finer control over data change tracking.

Types of CDC in PostgreSQL

Logical Replication

Logical replication enables table-level change tracking by streaming modifications to subscribers in real-time. This is ideal for replicating specific tables across environments.

WAL Streaming

Write-Ahead Logging (WAL) streaming captures all database transactions at the log level, providing high-performance, low-latency replication for disaster recovery and change tracking.

Trigger-Based CDC

Trigger-based CDC uses database triggers to log changes in a separate table. This approach allows for granular data tracking and is useful when selective changes need to be captured.

Polling-Based CDC

Polling-based CDC queries tables periodically to detect INSERT, UPDATE, DELETE operations by comparing timestamps or versioning fields. This method works well when direct replication mechanisms are unavailable.

Replication Tools for CDC

In addition to PostgreSQL's native CDC capabilities, several tools can be integrated to enhance replication and data synchronization:

• Debezium – A powerful open-source CDC tool that integrates with Kafka, PostgreSQL, and other databases for real-time data streaming.
• Striim – A real-time data integration platform that provides CDC pipelines between PostgreSQL and cloud environments.
• Airbyte – An open-source ELT tool that supports CDC for PostgreSQL and enables seamless data movement.
• Kafka Connect – Works with Debezium to capture and stream database changes into Kafka topics for further processing.
• PGSync – A tool that syncs PostgreSQL changes to search and analytics platforms like Elasticsearch.

💡 Tip: Choosing the right tool depends on the target system and the scale of replication required. If you need event-driven architectures, Kafka with Debezium is a great choice, whereas Airbyte works well for ETL processes.

Steps to Set Up CDC with PostgreSQL

1. Enable Logical Replication – Configure PostgreSQL to allow CDC.
2. Create a Replication Slot – Ensures that change logs are retained.
3. Stream Changes – Capture database modifications.
4. Apply Changes in Target Environment – Replicate changes in a lower environment.

⚠️ Warning: Enabling logical replication increases WAL storage usage. Regular monitoring and cleanup of replication slots is recommended.

Setting Up CDC in PostgreSQL

Enable logical replication in postgresql.conf, typically located in /etc/postgresql/<version>/main/postgresql.conf on Linux systems or C:\Program Files\PostgreSQL\<version>\data\postgresql.conf on Windows:

wal_level = logical
max_replication_slots = 4
max_wal_senders = 4

After making these changes, restart PostgreSQL using:

sudo systemctl restart postgresql  # Linux
net stop postgresql && net start postgresql  # Windows

Restart PostgreSQL, then create a replication slot:

SELECT * FROM pg_create_logical_replication_slot('guepard_slot', 'pgoutput');

To stream changes, query the slot:

SELECT * FROM pg_logical_slot_get_changes('guepard_slot', NULL, NULL);

💡 Tip: CDC can be combined with feature flagging to dynamically test new database changes without impacting production.

Using CDC to Create a Lower Environment in Guepard

CDC is particularly useful for creating lower environments that mirror production data. A lower environment is a non-production environment, such as staging or development, where developers and testers can safely experiment with real-world data without affecting live systems. With Guepard, you can:

• Provision a lower environment in seconds.
• Sync only relevant changes using CDC.
• Ensure isolation without impacting production.

Example: Provisioning a Lower Environment with Guepard

To create a lower environment from CDC logs, use Guepard’s API:

curl -X POST "https://api.guepard.run/deploy/${{DATABASE_ID}}/${{CLONE_ID}}/checkout?cdc=true"

• Deploys a database from CDC logs instead of full snapshots.
• Ensures fast provisioning while maintaining consistency.
• Allows developers to test with near-real-time production data.

💡 Tip: Use CDC filters to exclude sensitive data when provisioning lower environments.

📌 Note: For compliance-sensitive applications, ensure that sensitive data is masked or filtered when replicating changes with CDC.

Advantages of Using CDC in Guepard

⚡ Faster Syncs

• Traditional full dumps are slow and resource-intensive.
• CDC captures only changes, making updates instant and lightweight.

🔄 Real-Time Updates

• Keep lower environments synchronized with production.
• Ideal for testing, analytics, and debugging.

🏗️ Efficient Infrastructure

• Avoid full database clones that consume excess storage and computational resources. CDC reduces both storage overhead and processing costs by only tracking and applying changes instead of duplicating entire databases.
• Improve data consistency across environments.

SELECT slot_name, active FROM pg_replication_slots;

SELECT pg_drop_replication_slot('guepard_slot');

⚠️ Warning: CDC-based replication should be tested in a non-production environment before enabling it on live databases to avoid unexpected inconsistencies.

Guepard’s CDC integration with PostgreSQL allows teams to create real-time, synchronized lower environments efficiently. Whether for testing, analytics, or debugging, CDC ensures lightweight, accurate, and up-to-date data replication.