Learning Domain-Driven Design — Chapter 4
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What we discussed on the recording
The final section connects DDD with microservices, events, and Data Mesh. Microservices reflect autonomous teams and independent deployment. A good service is a deep module that hides complexity behind a small interface. A bounded context may define its boundary, but the mapping need not be one to one.
In an event-driven system, a command requests an action and may be rejected, while an event records a fact. Event Notification carries an identifier and requires a fresh read. Event-Carried State Transfer sends a snapshot or changes, reducing runtime dependency at the cost of stale data, replication, and schema coupling.
An analytical model differs from a transactional one. Star and Snowflake schemas organize facts and dimensions. A Data Warehouse transforms data on loading, but a shared model and ETL couple reports to services. A Data Lake delays transformation and enables many views, yet schema-less accumulation weakens data quality.
Data Mesh applies DDD to analytics: models follow bounded contexts, and domain teams own data. Data becomes a product with a versioned schema and quality expectations. A self-service platform simplifies publishing and discovery, while federated governance preserves shared rules and autonomy.