Change Data Capture (CDC): A Practical Guide for Real Systems

February 16, 2026

What You Will Learn

A Common Scenario

Many teams begin with a very reasonable architecture: PostgreSQL is the source of truth, while search, analytics, notifications, and internal tools consume copies of that data.

In the early stage, periodic polling is usually enough. A job runs every few minutes, reads what changed, and updates downstream systems.

As product usage grows, the same pattern starts to crack:

  • search results fall behind recent updates,
  • dashboards show numbers that do not match operational screens,
  • notifications arrive after the moment where they are most useful,
  • support teams see a different state than customers.

The important point is that this rarely comes from one broken query or one failing service. It is usually the result of several independent sync jobs, each with different intervals, retry logic, and failure behavior.

At that stage, the bottleneck is no longer query performance. The real problem is consistency and reliable data movement across systems.

Why Teams Choose CDC

When teams hit this point, there are usually two options:

  1. Keep polling and tune intervals, indexes, and batch sizes.
  2. Move to event-driven synchronization with Change Data Capture (CDC).

From a senior architecture perspective, the key question is not "can polling work?" It often can.

The real question is: what is the long-term cost of inconsistency across systems?

CDC wins when multiple systems depend on fresh state and PostgreSQL is the system of record. It gives you one reliable stream of committed changes instead of many ad-hoc readers.

How CDC Works in PostgreSQL

In practical terms, CDC transforms row-level database changes into events.

In PostgreSQL, the flow typically uses:

  • WAL (Write-Ahead Log) to record committed transactions,
  • logical decoding to translate WAL into readable events,
  • a connector (such as Debezium) to publish those events to consumers.

Flow in five steps:

  1. An INSERT, UPDATE, or DELETE is committed.
  2. PostgreSQL writes the change to WAL.
  3. The connector reads and decodes the entry.
  4. The event is published.
  5. Consumers update search, caches, analytics, and operational tools.

The key design advantage is consistency semantics: CDC emits committed state transitions. That is far safer than each service inventing its own polling strategy.

Where Teams Usually Struggle

The first CDC demo usually looks clean. The difficult parts appear later, with real traffic and frequent schema changes.

1) Snapshot to streaming handoff

Most connectors start with a snapshot, then continue with streaming.

If this transition is misconfigured, you can create duplicates or miss events.

2) Replication slots and WAL growth

Replication slots protect continuity, but they also retain WAL while consumers lag.

If a consumer stops for too long, WAL retention can grow and create disk pressure.

3) Ordering assumptions

Per-entity ordering is often manageable. Global ordering across all entities and consumers is rarely a realistic contract.

Your safety net is idempotent consumers, deterministic keys, and version-aware writes.

4) Delete semantics

Delete events are easy to ignore during early implementation.

Ignoring them creates stale cache entries and outdated search documents.

5) Schema evolution without governance

Many production issues are not connector bugs. They come from schema changes without compatibility planning.

Treat event schemas like public APIs: version, validate, and deprecate deliberately.

A Rollout Plan That Works

Avoid a big-bang migration.

Start with one business-critical table, prove correctness, then expand.

Recommended rollout:

  1. Choose one high-value table (orders is usually a good candidate).
  2. Define only the events that create clear business value.
  3. Integrate one consumer first (search or analytics).
  4. Measure lag, replay behavior, and data parity.
  5. Expand gradually, table by table, consumer by consumer.

Example change:

UPDATE orders
SET status = 'PAID'
WHERE id = 10231;

That single committed update can drive customer UI, fraud workflows, support visibility, and BI metrics without embedding fragile integration logic inside transactions.

Senior Architect Checklist

Before calling your CDC platform "production ready," confirm these seven points:

  1. Idempotency is mandatory

    • Every consumer handles duplicates and retries safely.

  2. Freshness is observable end to end

    • You track connector lag, consumer lag, and business freshness SLOs.

  3. Events are explicit contracts

    • Schema ownership, versioning rules, and migration windows are defined.

  4. Delete handling is tested

    • Tombstones and cleanup paths are validated for each consumer.

  5. Replay is operationalized

    • Teams can rebuild consumers from history with documented runbooks.

  6. PostgreSQL safety limits exist

    • Replication slot lag and WAL growth thresholds have alerts and escalation.

  7. Raw CDC and domain events are separated when needed

    • You enrich low-level table events when business consumers need cleaner language.

When Not to Use CDC

CDC is a strong architectural pattern, not a default for every project.

  • Batch jobs / polling are still fine for low-frequency reporting.
  • Database triggers can solve small local integrations quickly.
  • Transactional Outbox is often better when the application owns business events.
  • Event Sourcing fits domains where event history is the primary model.

Use CDC when freshness matters, multiple consumers depend on the same source of truth, and your team is ready to operate event contracts with discipline.

Final Thoughts

In practice, teams succeed with CDC when ownership is clear and operations are disciplined.

CDC with PostgreSQL is powerful, but its real value appears when you pair it with strong engineering habits: explicit contracts, observability, replay capability, and gradual rollout.

That is how you turn a stream of database changes into a trustworthy platform capability.

If you want a runnable reference, check this project: