Change Data Capture (CDC)#

Every database mutation tells a story. Change Data Capture turns those mutations into a real-time event stream — enabling downstream systems to react to changes the instant they happen.

Why CDC Matters#

Traditional data integration relies on batch ETL jobs that run hourly or nightly. CDC flips this model:

Batch ETL:   Source DB → (wait hours) → ETL job → Target
CDC:         Source DB → (milliseconds) → Stream → Target

Use cases that demand CDC:

• Real-time analytics — dashboards that reflect the last second, not last hour
• Cache invalidation — update Redis the moment a row changes
• Search index sync — keep Elasticsearch in lockstep with your database
• Microservice data propagation — share state without coupling services
• Audit logs — capture every mutation for compliance

CDC Patterns#

1. Log-Based CDC#

Databases already record every change in a write-ahead log (WAL) or binary log. Log-based CDC taps into this stream directly.

PostgreSQL WAL / MySQL binlog / MongoDB oplog
        │
        ▼
   CDC Connector (Debezium)
        │
        ▼
   Kafka / Event Stream
        │
        ▼
   Consumers (analytics, cache, search)

Advantages:

• Zero impact on source database performance
• Captures every change (no missed updates between polls)
• Preserves operation type (INSERT, UPDATE, DELETE)
• Includes before-and-after snapshots of rows

Debezium example configuration:

{
  "name": "inventory-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "db.example.com",
    "database.port": "5432",
    "database.user": "cdc_user",
    "database.dbname": "inventory",
    "table.include.list": "public.orders,public.products",
    "topic.prefix": "inventory",
    "plugin.name": "pgoutput",
    "slot.name": "debezium_slot"
  }
}

2. Trigger-Based CDC#

Database triggers fire on INSERT, UPDATE, or DELETE and write change records to a shadow table.

CREATE TRIGGER orders_cdc_trigger
AFTER INSERT OR UPDATE OR DELETE ON orders
FOR EACH ROW
EXECUTE FUNCTION capture_change();

Trade-offs:

• Works on any database that supports triggers
• Adds write overhead to every mutation
• Requires schema changes (shadow tables)
• Can miss changes from bulk operations or schema migrations

3. Polling (Timestamp-Based)#

A process periodically queries for rows with updated_at greater than the last checkpoint.

SELECT * FROM orders
WHERE updated_at > :last_checkpoint
ORDER BY updated_at ASC
LIMIT 1000;

Trade-offs:

• Simple to implement — no special database permissions needed
• Misses deletes entirely (no row to query)
• Cannot detect multiple rapid updates to the same row
• Polling interval creates inherent latency

4. Comparison of Patterns#

┌─────────────────┬────────────┬──────────┬─────────────┐
│ Pattern         │ Latency    │ DB Load  │ Delete Aware │
├─────────────────┼────────────┼──────────┼─────────────┤
│ Log-based       │ ~seconds   │ None     │ Yes          │
│ Trigger-based   │ ~seconds   │ Medium   │ Yes          │
│ Polling         │ ~minutes   │ High     │ No           │
└─────────────────┴────────────┴──────────┴─────────────┘

The Dual-Write Problem#

The most dangerous anti-pattern in distributed systems: writing to two systems and assuming both succeed.

// DANGEROUS — dual write
await database.save(order);
await kafka.publish("order.created", order);
// What if Kafka publish fails? Systems are now inconsistent.

CDC eliminates dual writes by making the database the single source of truth. Downstream systems read from the CDC stream — you only write to one place.

// SAFE — single write + CDC
await database.save(order);
// Debezium captures the INSERT from the WAL
// Kafka consumers receive the event automatically

Outbox Pattern#

When you need guaranteed event publishing alongside a database write, use the transactional outbox:

BEGIN TRANSACTION;
  INSERT INTO orders (id, ...) VALUES (...);
  INSERT INTO outbox (aggregate_id, event_type, payload)
    VALUES (order_id, 'OrderCreated', '{"..."}');
COMMIT;

Debezium reads the outbox table via CDC and publishes events to Kafka. Both the order and the event are committed atomically.

Event Sourcing via CDC#

CDC enables a pragmatic path to event sourcing without rewriting your application:

Traditional DB (CRUD)
     │
     ▼ CDC stream
     │
     ▼
Event Store / Kafka topic (append-only log)
     │
     ▼
Materialized views, projections, read models

This gives you event sourcing benefits — full audit trail, temporal queries, replay — while keeping your existing CRUD application.

CDC Tools Comparison#

Debezium#

• Open-source, Kafka Connect-based
• Supports PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, Cassandra
• Exactly-once semantics with Kafka transactions
• Most mature and widely adopted

Maxwell#

• Lightweight MySQL-only CDC
• Reads MySQL binlog, outputs JSON to Kafka, Kinesis, or stdout
• Simpler setup than Debezium for MySQL-only environments

DynamoDB Streams#

• Native CDC for AWS DynamoDB
• 24-hour retention window
• Integrates with Lambda for serverless processing
• Guaranteed ordering per partition key

Additional Tools#

• AWS Database Migration Service (DMS) — managed CDC for AWS databases
• Google Datastream — serverless CDC for BigQuery and Cloud SQL
• Striim — enterprise CDC with built-in transformations
• Airbyte — open-source ELT with CDC connectors

Real-Time Sync Architecture#

A production CDC pipeline for keeping search and cache in sync:

PostgreSQL
   │
   ▼ Debezium (WAL reader)
   │
   ▼ Kafka (orders.cdc topic)
   │
   ├──▶ Elasticsearch Sink Connector → search index
   ├──▶ Redis Sink Connector → cache layer
   ├──▶ Analytics Consumer → data warehouse
   └──▶ Notification Service → user alerts

Key operational concerns:#

Schema evolution — use a schema registry (Confluent or Apicurio) to manage Avro/Protobuf schemas as your tables evolve.

Snapshotting — when you first start a CDC connector, it performs an initial snapshot of existing data before streaming changes.

Exactly-once delivery — combine Kafka transactions with idempotent consumers to prevent duplicate processing.

Monitoring — track replication lag, connector status, and consumer group offsets. Alert when lag exceeds your SLA.

When NOT to Use CDC#

• Simple CRUD apps with a single database and no downstream consumers
• Batch analytics where hourly freshness is acceptable (cheaper to run scheduled queries)
• Databases without WAL access — some managed databases restrict replication slot access

Quick Start Checklist#

1. Enable logical replication on your database (PostgreSQL: wal_level = logical)
2. Create a dedicated CDC user with replication permissions
3. Deploy Debezium via Kafka Connect or Debezium Server
4. Configure table filters to capture only what you need
5. Set up a schema registry for schema evolution
6. Build idempotent consumers that handle duplicates gracefully
7. Monitor replication lag and connector health

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Change Data Capture transforms your database into a real-time event source — solving the dual-write problem, enabling event-driven architectures, and keeping every downstream system in sync. Start with log-based CDC via Debezium and expand from there.

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