data-stack-delivery
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-engineeringwhen the main decision is platform shape, semantic metrics, marts, or multi-team ownership.
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