PostgreSQL CDC for Real-Time Data Replication

In the era of real-time data, Change Data Capture (CDC) in PostgreSQL has become a critical capability for organizations aiming to sync systems, trigger events, and power analytics with fresh, consistent data. This guide will take you through the core concepts, methods, tools, and best practices of how to enable CDC in PostgreSQL instance, making it easier for you to build efficient, reliable, and scalable data pipelines.

What is PostgreSQL CDC?

Change Data Capture (CDC) is a technique to monitor and capture changes—such as inserts, updates, and deletes—from a PostgreSQL database in real time or near-real time. These data changes are then delivered to downstream systems, enabling:

• Real-time analytics, and dashboards

• Event-driven applications

• Data synchronization across microservices

• ETL/ELT workflows for modern data stacks

CDC in PostgreSQL: Methods Compared

1. Logical Replication (PostgreSQL 10+)

Best For: Selective replication, real-time data movement

Native logical replication in PostgreSQL server uses the WAL (Write-Ahead Log) to replicate changes. You can replicate individual database tables by creating publications and subscriptions.

Pros:

• Native support, minimal overhead

• High-performance streaming

Cons:

• Doesn’t support schema changes or sequences

• Setup requires PostgreSQL versions above 10

2. Trigger-Based CDC

Best For: Auditing, custom logging

This involves adding AFTER INSERT/UPDATE/DELETE triggers on tables to log changes into separate audit tables.

Pros:

• Full control over captured data

• Easy to extend with business logic

Cons:

• Performance impact on large tables

• Maintenance-heavy

3. Timestamp-Based Polling

Best For: Lightweight incremental pulls

Add a last_modified timestamp column and query for recently updated rows.

Pros:

• Simple to implement

• Low-tech entry point

Cons:

• Can miss deletes

• Relies on accurate time synchronization

4. WAL-Based Tools (e.g. Debezium)

Best For: Streaming CDC with minimal impact

Debezium PostgreSQL CDC source connector and similar tools tap directly into WAL, converting it into change events that can be streamed (often to Kafka).

Pros:

• Non-invasive, high throughput

• Event-based architecture friendly

Cons:

• Requires Kafka or similar infrastructure

• More complex deployment

Top PostgreSQL CDC Tools in 2025

Best Practices for PostgreSQL CDC Implementation

1. Start with Schema Design
    Include metadata columns (last_updated, operation_type) if using triggers or polling.

2. Use WAL for Performance
    For production workloads, prefer WAL-based CDC for minimal impact and better throughput.

3. Secure Your Pipelines
   Encrypt data in transit (TLS) and at rest.
   Consider field-level encryption for sensitive data using tools like Integrate.io.

4. Ensure Idempotency
    Use primary keys and operation markers to ensure data integrity in targets.

5. Monitor & Scale
    Track replication lag (pg_stat_replication) and deploy auto-scaling data sinks when necessary.

Real-World PostgreSQL CDC Use Cases

• E-commerce: Sync inventory changes from Postgres tables to Elasticsearch for real-time search.

• Finance: Audit logs of transactions using trigger-based CDC.

• Healthcare: Replicate sensitive data sets securely with field-level encryption and necessary permissions.

• SaaS Apps: Use the functionality to enable live dashboards by streaming data from PostgreSQL and other data sources to Redshift or BigQuery.

Future of PostgreSQL CDC

With the rise of event-driven architectures, data mesh, and real-time analytics, CDC is evolving beyond simple data sync. Expect tighter integration with serverless pipelines, schema evolution handling, and declarative CDC configs.

Summary: How to Get PostgreSQL CDC Right

Implementing CDC isn’t just about moving data—it’s about building trust in the freshness and accuracy of that data. Whether you're modernizing legacy systems, building real-time analytics, or syncing multi-cloud environments, PostgreSQL CDC has the tooling and flexibility you need.

Conclusion

PostgreSQL Change Data Capture (CDC) is no longer a niche requirement—it's a foundational element for building agile, real-time, and insight-driven systems. From understanding core methods like logical replication, trigger-based logging, and timestamp-based polling, to leveraging modern tools such as Debezium, Airbyte, Hevo, AWS DMS, and Integrate.io, the path to efficient real-time CDC is now more accessible than ever.

Each method has its strengths depending on latency needs, scalability, and system complexity. By following best practices—like schema planning, encryption, idempotency, and monitoring—you can ensure data consistency and system reliability regardless of the data type, like JSON. Whether you're syncing operational systems, powering real-time dashboards, or modernizing legacy ETL, adopting the right CDC strategy will make your data infrastructure future-ready. Invest in the right tools, design thoughtfully, and you’ll turn PostgreSQL CDC into a competitive advantage for your business.

If you're building real-time data replication pipelines with PostgreSQL source database or MYSQL, or custom APIs, get in touch with Integrate.io ETL experts. We’ll help you design, scale, and secure your CDC workflows—fast. And, you can use data stores like data warehouses like Azure for seamless downstream applications.

FAQs

Q: Does PostgreSQL have CDC?

Yes, PostgreSQL source tables supports Change Data Capture (CDC) through several methods, including query-based, trigger-based, and log-based (logical replication) approaches. Log-based Postgres CDC using logical replication is the most popular and efficient method for real-time change capture.

Q: What is the best CDC for Postgres?

Log-based CDC using PostgreSQL's logical replication (WAL/logical decoding) is generally considered the best approach for real-time, efficient, and low-impact change capture. Tools like Debezium (often used via connectors in platforms like Confluent or Microsoft Fabric) are widely regarded as industry standards for implementing log-based CDC in PostgreSQL.

Q: What is the CDC connector in PostgreSQL?

The CDC connector in PostgreSQL commonly refers to connectors like Debezium PostgreSQL CDC Source, which reads database changes from PostgreSQL's write-ahead log (WAL) using logical decoding and streams them to downstream systems (e.g., Kafka, event streams). Microsoft Fabric and Confluent both offer CDC connectors for Postgres database based on Debezium.

Q: What is CID in PostgreSQL?

CID in PostgreSQL stands for Command Identifier. It is an internal identifier used by PostgreSQL to distinguish between different commands within a single transaction. The following commands will help you primarily for internal concurrency control and visibility of changes within a transaction, not for CDC processes or external data capture.