How do you ensure data consistency across microservices?

Because each microservice has its own database, achieving data consistency across services is one of the hardest challenges in microservices. The primary approach is eventual consistency — accepting that data will be temporarily inconsistent between services but will converge to a consistent state given enough time. The main patterns are: Saga pattern for managing multi-step business transactions with compensating rollbacks; Event-driven synchronization where services publish domain events and others update their own local copies of the data; and Idempotency to safely handle duplicate message delivery. The key is to design business processes that tolerate temporary inconsistency — for example, showing "pending" status rather than requiring immediate strong consistency for every operation.