Data in Microservices

Managing data across multiple independent services is fundamentally different from monolithic architectures. Each service needs its own data storage, boundaries, and consistency mechanisms. This section covers the key patterns for handling data in distributed systems.

📄️ Database Per Service

Isolate service data with dedicated databases to maintain independence and prevent tight coupling.

📄️ Transaction Boundaries and Sagas

Coordinate distributed transactions across services using choreography and orchestration patterns.

📄️ Outbox, Inbox, and Change Data Capture

Ensure reliable event publishing and delivery with outbox, inbox, and CDC patterns.

📄️ CQRS and Read Models

Separate read and write models to optimize for different access patterns and enable flexible data transformation.

📄️ Event Sourcing

Store the complete history of changes as immutable events rather than storing only the current state.

📄️ Data Replication, Sharding, and Partitioning

Scale data storage horizontally by distributing data across multiple databases and replicas.

📄️ Schema Evolution and Versioning

Evolve data schemas safely without breaking clients as systems change over time.