1οΈβ£ What is CDC (Change Data Capture)?
CDC is a mechanism to capture and stream database changes (INSERT, UPDATE, DELETE) in real time, typically by reading transaction logs, to keep systems synchronized efficiently.
π Instead of repeatedly scanning the whole database, CDC only captures what changed, making systems more efficient.
2οΈβ£ Why do we use CDC?
π Key Use Cases
- Real-time data pipelines
- Send DB changes to Kafka, analytics systems, etc.
- Microservices sync
- Keep multiple services/databases in sync
- Data warehousing
- Replicate production DB β data warehouse
- Audit logs
- Track who changed what and when
3οΈβ£ How CDC Works (Conceptually)
When a change happens in DB:
User updates row β DB logs change β CDC captures change β sends to consumers
π Common ways to implement CDC
1. Log-based CDC (β Best)
- Reads database transaction logs
- Example tools:
- Debezium
- Apache Kafka (with connectors)
- π Pros:
- No performance impact on DB
- Captures all changes reliably
2. Trigger-based CDC
- DB triggers fire on INSERT/UPDATE/DELETE
- Store changes in another table
- π Pros:
- Easy to implement
- β οΈ Cons:
- Slows DB
- Hard to scale
3. Timestamp / Polling-based CDC
- Query rows where
updated_at > last_checked_time - π Pros:
- Simple
- β οΈ Cons:
- Misses deletes
- Not truly real-time
4οΈβ£ Simple Example
Table:
users
id | name | updated_atChange:
UPDATE users SET name = "Omi" WHERE id = 1;CDC Output Event:
{
"operation": "UPDATE",
"before": { "id": 1, "name": "Om" },
"after": { "id": 1, "name": "Omi" },
"timestamp": "2026-04-14T04:00:00Z"
}5οΈβ£ Where CDC fits in System Design
Architecture flow:
ββββββββββββββββββββββββ
β Application β
β (User / Service API)β
ββββββββββββ¬βββββββββββ
β
βΌ
ββββββββββββββββββββββββ
β Database β
β (MySQL / Postgres) β
ββββββββββββ¬βββββββββββ
β
(Writes: INSERT / UPDATE / DELETE)
β
βΌ
ββββββββββββββββββββββββ
β Transaction Logs β
β (Binlog / WAL) β
ββββββββββββ¬βββββββββββ
β
(Log-based CDC reads)
β
βΌ
ββββββββββββββββββββββββ
β CDC Tool β
β (Debezium) β
ββββββββββββ¬βββββββββββ
β
(Convert to events)
β
βΌ
ββββββββββββββββββββββββ
β Message Broker β
β (Kafka) β
ββββββββββββ¬βββββββββββ
β
βββββββββββββββββ¬ββββββββββββββββ¬ββββββββββββββββ
βΌ βΌ βΌ βΌ
ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ
β Analytics β β Cache β β Search Index β β Notificationsβ
β (Warehouse) β β (Redis) β β(Elastic) β β Service β
ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ
6οΈβ£ Key Benefits
- Real-time data sync
- Efficient (no full table scans)
- Event-driven architecture
- Enables streaming systems