pipeline-design

Designing a reliable data pipeline involves structuring ingestion, storage, transformation, and serving layers with robust orchestration and clear dependency management. This Skill generates complete pipeline plans, including DAG definitions and architecture diagrams for observable outputs.

Choosing an orchestrator for dbt transformation pipelines depends on your team's needs. This Skill provides guidance to select and configure tools like Airflow, Dagster, Prefect, or dbt Cloud to manage your data pipeline dependencies.

Yes, you can use Airflow or Dagster to manage complex dependencies in your data pipeline. This Skill helps implement design patterns like linear, fan-out/fan-in, and data asset patterns to handle complex dependency flows.

Adding observability patterns to an existing dbt and Airflow pipeline ensures reliable failure handling across all layers. This Skill helps redesign current pipelines to be observable and cost-efficient, outputting updated architecture diagrams.

Design patterns that help manage complex dependencies in orchestrated data pipelines include linear, fan-out/fan-in, and data asset patterns. These patterns structure your dbt transformations to maintain clear dependency management.