⚡AgentSkills
🛢️ Data Engineering · Streaming & Modeling

Apply dimensional modeling pragmatically

Facts, dimensions, SCD strategy, and grain declarations serving analytics for years.

intermediate~35 minData EngineersAnalytics EngineersPlatform Data Teams

Steps

  1. 1Declare the grain of every fact table in its documentation header
  2. 2Conform shared dimensions across facts before building marts
  3. 3Pick SCD type deliberately: type-2 for history that matters, nothing else
  4. 4Prefer wide tables for specific products over generic mega-marts
  5. 5Add surrogate keys where natural keys churn
  6. 6Model for the top 10 analyst questions; revisit quarterly

Common Pitfalls

  • ▲Mixed-grain fact tables producing wrong aggregates forever
  • ▲Type-1 updates silently erasing history compliance needed

Commands

Install with skills CLI
$ npx skills add aniruddhaadak80/skills --skill streaming-modeling-dimensional-modeling-refresh
Install globally
$ npx skills add aniruddhaadak80/skills --skill streaming-modeling-dimensional-modeling-refresh -g

Tags

#modeling#kimball#analytics#data-engineering#streaming-modeling

Related skills

Choose windowing and watermarks correctlyadvanced

Event-time vs processing-time decisions, late-data handling, and exactly-once semantics.

🛢️ Data Engineering·~40m
Open →