architecting-data

From sumik

Data architecture patterns covering read-side optimization (replicas, materialized views, CQRS, CDC, event sourcing), domain-based decomposition, polyglot persistence, and caching strategies (cache-aside, read-through, write-through, write-around). Use when designing data flow architecture, choosing read scalability strategies, or implementing caching for enterprise systems. For microservices patterns (Saga, granularity), use architecting-infrastructure instead. For DDD domain modeling, use applying-clean-architecture instead. For database engine internals, use developing-databases instead. For relational DB schema design, use developing-databases instead. For GCP-specific data services (BigQuery, Dataflow, Dataproc), lakehouse (BigLake, Dataplex), and BigQuery advanced operations (editions, HA/DR), use developing-google-cloud instead.

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