database-designer
Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.
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enterprise-data-integration-and-distribution
Pick the right enterprise integration pattern: APIs (REST, SOA, ROA), event-driven (message queues, event brokers, event streams, CEP), CQRS, Backend-for-Frontend, composite services, choreography vs orchestration, point-to-point exceptions, queue-based load leveling. Apply to data distribution: batch vs API vs event-based ingestion. Use when designing cross-domain data flow, choosing sync vs async, deciding between message queue and event stream, or selecting between orchestration and choreography. Triggers: "REST or events", "queue vs stream", "CQRS for data", "BFF pattern", "service orchestration vs choreography", "composite service", "data distribution patterns", "event-driven for data". Produces a chosen integration approach with rationale.
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Apply data storage and modeling patterns: cache hierarchy, consistency paradigms (strong vs eventual), file/object/block storage, warehouse vs lake vs lakehouse, ingestion patterns (batch, streaming, CDC, snapshot vs differential), schema-on-write vs schema-on-read, dimensional modeling (Kimball star schema, Inmon 3NF, Data Vault), Slowly Changing Dimensions (SCD types 1/2/3), and distributed-query patterns (broadcast vs shuffle hash join, MapReduce). Use when designing storage layers, modeling a warehouse, choosing ingestion frequency, or evaluating a transformation approach. Triggers: "warehouse vs lake", "Kimball vs Inmon vs Data Vault", "SCD type 2", "schema on read", "CDC vs scheduled extract", "broadcast join", "data lakehouse", "Iceberg / Delta / Hudi". Produces a chosen storage architecture + data model with rationale.
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Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan.
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Apply Master Data Management (MDM) styles (Consolidation, Registry, Centralized, Coexistence), federated governance via data contracts and policy automation, data catalog + metalake architecture, knowledge graphs for metadata, semantic layers, and access control models (ACL, RBAC, ABAC + PEP/PDP/PIP/PAP). Use when scoping MDM, choosing an MDM style, designing a data catalog, building governance automation, defining data contracts, or implementing fine-grained access control on data products. Triggers: "MDM strategy", "consolidation vs registry vs centralized vs coexistence", "data contract", "data catalog", "knowledge graph for metadata", "ABAC for data", "semantic layer for governance", "metalake". Produces a chosen MDM style + governance architecture with policy automation.
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