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.
Repository SourceNeeds Review
data-storage-and-modeling-patterns
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.
Safety Notice
This listing is imported from SkillsMP metadata and should be treated as untrusted until upstream source review is completed.
Copy this and send it to your AI assistant to learn
Install skill "data-storage-and-modeling-patterns" with this command: npx skills add AlexYedi/skillsmp-alexyedi-alexyedi-data-storage-and-modeling-patterns
No markdown body
This source entry does not include full markdown content beyond metadata.
Source Transparency
This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.
Related Skills
Related by shared tags or category signals.
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.
Repository SourceNeeds Review
data-quality-auditor
Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan.
Repository SourceNeeds Review
mdm-and-federated-data-governance
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.
Repository SourceNeeds Review