data-intensive-patterns

Generate data-intensive application code by providing a design prompt that specifies your workload and preferred ecosystem, requesting a code scaffold for storage, replication, partitioning, or processing components.

Review event sourcing and CDC architectures by submitting your design or code for systematic evaluation against established data-intensive patterns, identifying anti-patterns and proposing structural improvements.

Architect data-heavy apps by applying Martin Kleppmann's design philosophy, which provides structured patterns for stream processing, partitioning, and derived data tasks to ensure systems handle scale effectively.

Support both code generation and critical design review through a built-in decision flow that guides you to either scaffold end-to-end components or systematically evaluate existing data-heavy system code.

Apply CQRS patterns when separating read and write models for data-intensive workloads, using the skill's patterns catalog to determine if event sourcing and stream processing fit your specific scale requirements.