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