How do you handle data migration between microservices?

In a microservices architecture, data migration refers to the process of transferring or synchronizing data between services, which is a key requirement for handling database isolation of independent services. Its importance lies in supporting application scalability, reducing coupling, and ensuring business continuity, and it is commonly found in event-driven scenarios such as user data updates or order processing. Application scenarios include distributed environments like data integration and API calls.

The core principles include using asynchronous communication (such as message queue Kafka), transaction management to ensure eventual consistency, and avoiding direct dependencies. The core components cover the event sourcing pattern, data pipeline design, and API gateway implementation. In practical applications, data is migrated through an event-driven architecture (such as CDC Change Data Capture), with impacts including improving system resilience, supporting multi-active deployment, and simplifying maintenance.

Processing steps: 1. Identify the data sources and target services that need to be migrated. 2. Deploy an event queue (such as RabbitMQ) to capture data changes. 3. Synchronize data through batch processing or real-time scripts. A typical scenario is migrating a user service to an order service, with business values including reducing lock contention, accelerating iteration, and enhancing data isolation.