Data Migration Strategies: Dual Writes, CDC, and Tools like Debezium

Data migration involves transferring data between systems while minimizing downtime and ensuring consistency. Below are key strategies, tools, and commands for effective data migration.

. Dual Writes
Description:
The application writes data to both the source and target systems simultaneously. This ensures both systems stay in sync during migration.

Pros:

Simple to implement at the application layer.

Real-time synchronization.

Cons:

Risk of data inconsistency if one write fails.

Requires handling retries, idempotency, and error recovery.

Increased latency due to dual operations.

Example Workflow:

Modify application logic to write to both databases.

Implement reconciliation processes to resolve discrepancies.

Gradually shift read traffic to the new system.

Commands:
No specific commands (application-dependent), but pseudocode logic:

def write_data(data):
    try:
        source_db.write(data)
        target_db.write(data)
    except Exception as e:
        retry_or_rollback(data)

Change Data Capture (CDC)
Description:
Captures database changes (inserts/updates/deletes) via transaction logs (e.g., MySQL binlog, PostgreSQL WAL) and streams them to the target system.

Pros:

Decoupled from application logic.

Low latency and reliable (avoids dual-write pitfalls).

Supports large-scale migrations.

Cons:

Requires database log access.

May need schema/data type conversions.

Tools:

Debezium: Open-source CDC tool built on Apache Kafka.

AWS Database Migration Service (DMS): Managed service for homogeneous/heterogeneous migrations.

pglogical: PostgreSQL-specific logical replication.

1. Debezium Setup Example Steps:

Start Kafka Connect with Debezium connectors:

bin/connect-standalone.sh config/connect-standalone.properties config/debezium-mysql.properties

-- Register a MySQL Connector (via curl):

curl -X POST -H "Content-Type: application/json" \
--data @mysql-config.json http://localhost:8083/connectors

Example mysql-config.json:

{
  "name": "mysql-connector",
  "config": {
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",
    "database.hostname": "localhost",
    "database.port": "3306",
    "database.user": "user",
    "database.password": "password",
    "database.server.id": "184054",
    "database.server.name": "migration-server",
    "database.include.list": "mydb",
    "table.include.list": "mydb.users",
    "database.history.kafka.bootstrap.servers": "localhost:9092"
  }
}

-- Stream changes to a target database using a Kafka sink connector (e.g., JDBC sink).

-- Data Transfer Commands
MySQL Dump/Restore:

# Export
mysqldump -u root -p mydb > mydb_dump.sql

# Import
mysql -u root -p mydb < mydb_dump.sql

PostgreSQL Dump/Restore:

# Export
pg_dump -U postgres -d mydb > mydb_dump.sql

# Import
psql -U postgres -d mydb -f mydb_dump.sql

AWS DMS CLI (create replication task):

aws dms create-replication-task \
--replication-task-identifier "my-task" \
--source-endpoint-arn <source_arn> \
--target-endpoint-arn <target_arn> \
--replication-instance-arn <instance_arn> \
--migration-type full-load-and-cdc \
--table-mappings file://mappings.json

-- Key Considerations

Dual Writes: Use for simple, small-scale migrations with strong error handling.
CDC: Ideal for zero-downtime migrations; pair with tools like Debezium.
Schema Changes: Handle schema evolution (e.g., Avro schemas in Kafka).
Validation: Verify data consistency post-migration with checksum tools.
By combining strategies (e.g., initial dump + CDC for ongoing changes), teams can achieve seamless migrations. Choose tools based on database type, scalability needs, and infrastructure complexity.