Change Data Capture (CDC) – A Complete Guide to Real-Time Data Synchronization

By /

Listen to this Post

Featured Image
Change Data Capture (CDC) is a powerful technique that revolutionizes data management by capturing and processing changes in real time. It enables real-time analytics, seamless synchronization, and efficient data warehousing while reducing the load on source systems.

Approaches to CDC Replication

  1. Log-based CDC: Monitors database transaction logs (e.g., MySQL binlog, PostgreSQL WAL).
  2. Trigger-based CDC: Uses database triggers to track changes.
  3. Polling-based CDC: Periodically checks source tables for updates.

Critical Steps in CDC Replication

  1. Initial Snapshot: Captures the current state of source data.
  2. Capturing Change Events: Continuously monitors for INSERT, UPDATE, DELETE operations.
  3. Transforming & Loading Changes: Applies changes to target systems efficiently.

Best Practices for CDC Implementation

  • Choose the right CDC method based on latency and performance needs.
  • Ensure data integrity with transaction checks.
  • Monitor CDC pipelines for failures and delays.
  • Handle schema changes gracefully to avoid pipeline breaks.

    You Should Know: Practical CDC Implementation with Commands & Code

1. Log-Based CDC with MySQL

Enable binary logging in MySQL:

-- Check if binary logging is enabled 
SHOW VARIABLES LIKE 'log_bin';

-- Enable binary logging in my.cnf 
[bash] 
log-bin=mysql-bin 
binlog-format=ROW

2. Debezium for Real-Time CDC

Debezium (Kafka-based CDC tool) setup:

 Start Zookeeper & Kafka 
bin/zookeeper-server-start.sh config/zookeeper.properties 
bin/kafka-server-start.sh config/server.properties

Run Debezium MySQL connector 
curl -i -X POST -H "Accept:application/json" -H "Content-Type:application/json" http://localhost:8083/connectors/ -d @mysql-connector.json

Example `mysql-connector.json`:

{
"name": "inventory-connector",
"config": {
"connector.class": "io.debezium.connector.mysql.MySqlConnector",
"database.hostname": "mysql",
"database.port": "3306",
"database.user": "debezium",
"database.password": "dbz",
"database.server.id": "184054",
"database.server.name": "dbserver1",
"database.include.list": "inventory",
"database.history.kafka.bootstrap.servers": "kafka:9092",
"database.history.kafka.topic": "schema-changes.inventory"
}
}

3. Trigger-Based CDC in PostgreSQL

Create a trigger to log changes:

CREATE TABLE audit_log (
id SERIAL PRIMARY KEY,
table_name TEXT,
operation TEXT,
old_data JSONB,
new_data JSONB,
change_time TIMESTAMP
);

CREATE OR REPLACE FUNCTION log_changes() RETURNS TRIGGER AS $$
BEGIN
IF TG_OP = 'INSERT' THEN
INSERT INTO audit_log (table_name, operation, new_data, change_time)
VALUES (TG_TABLE_NAME, 'INSERT', to_jsonb(NEW), NOW());
ELSIF TG_OP = 'UPDATE' THEN
INSERT INTO audit_log (table_name, operation, old_data, new_data, change_time)
VALUES (TG_TABLE_NAME, 'UPDATE', to_jsonb(OLD), to_jsonb(NEW), NOW());
ELSIF TG_OP = 'DELETE' THEN
INSERT INTO audit_log (table_name, operation, old_data, change_time)
VALUES (TG_TABLE_NAME, 'DELETE', to_jsonb(OLD), NOW());
END IF;
RETURN NULL;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER track_changes
AFTER INSERT OR UPDATE OR DELETE ON your_table
FOR EACH ROW EXECUTE FUNCTION log_changes();

4. Polling-Based CDC with Python

import psycopg2
import time

def poll_changes():
conn = psycopg2.connect("dbname=test user=postgres")
cursor = conn.cursor()
last_id = 0

while True:
cursor.execute("SELECT  FROM orders WHERE id > %s ORDER BY id", (last_id,))
rows = cursor.fetchall()

for row in rows:
print(f"New change detected: {row}")
last_id = row[bash]

time.sleep(5)  Poll every 5 seconds

poll_changes()

5. Monitoring CDC Latency

 Check Kafka consumer lag (Debezium) 
kafka-consumer-groups --bootstrap-server localhost:9092 --group debezium --describe

PostgreSQL WAL monitoring 
SELECT pg_current_wal_lsn(), pg_wal_lsn_diff(pg_current_wal_lsn(), replay_lsn) 
FROM pg_stat_replication;

What Undercode Say

CDC is essential for modern data architectures, enabling real-time decision-making. Log-based CDC (Debezium, MySQL binlog) is the most efficient, while trigger-based adds overhead. Always monitor replication lag and validate data consistency.

Expected Output:

  • Real-time data sync between databases.
  • Efficient ETL pipelines with minimal source load.
  • Auditable change logs for compliance.

Prediction

As data volumes grow, CDC will become the standard for real-time analytics, replacing batch ETL in most enterprises. AI-driven CDC optimizations (auto-tuning, anomaly detection) will emerge.

Relevant URLs:

References:

Reported By: Im Nsk – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

Join Our Cyber World:

💬 Whatsapp | 💬 Telegram

Related Posts: