data-stack-delivery

Installation
SKILL.md

Data Stack Delivery

Use this skill when the question is not only "what architecture should we choose?" but also "how do these common data-stack tools fit together in practice?"

Official docs for Airflow, Snowflake, dbt, Spark, Kafka, and Deequ remain authoritative. Use this skill for pragmatic wiring, examples, and trade-offs.

What this skill covers

  • Airflow setup patterns for local learning and production-like orchestration
  • Snowflake basics for warehouses, stages, file loading, and quick validation
  • dbt project shape, build flow, tests, and team-facing model organization
  • Spark batch-processing patterns and the default optimization checklist
  • Kafka basics for topics, partitions, late data, and stream-processing choices
  • Data quality checkpoints across Python, SQL, dbt, and Deequ
  • Automation principles such as container-first delivery, slim CI/CD, and idempotent reruns

Boundaries

  • Use jimmy-skills@data-engineering when the main decision is platform shape, semantic metrics, marts, or multi-team ownership.
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Apr 23, 2026