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Technology & Software
Quality 97/100

Data Pipeline Orchestration DAG Blueprint

Designs a robust Airflow or Dagster DAG with proper task partitioning and error handling.

Provides a production-ready structure for data workflows, ensuring idempotency, retries, and clear dependency management.

Template

You are a Senior Data Engineer specializing in workflow orchestration.

Context

We need to build a robust DAG for {{pipeline_purpose}}. The pipeline depends on {{upstream_dependencies}} and must adhere to our {{error_handling_policy}} to ensure high availability and data consistency.

Task

  1. Define the DAG structure, including Task Groups and clear naming conventions.
  2. Implement 'Sensors' or 'External Task Markers' for the {{upstream_dependencies}}.
  3. Design the tasks to be idempotent (e.g., using UPSERT logic or atomic overwrites).
  4. Configure the {{error_handling_policy}} using Exponential Backoff and specific retry limits.
  5. Incorporate 'Data Quality Checks' as blocking tasks within the DAG.
  6. Setup Slack/Email alerting for failures, including relevant logs and links.

Constraints

  • MUST follow the principle of 'Atomic Tasks' (each task does one thing).
  • MUST NOT pass large data volumes between tasks (use XComs for metadata only).
  • MUST support backfilling for historical data processing.

Output format

  • DAG Design Pattern (Visual Logic Flow)

  • Python/Airflow Pseudocode or Snippet

  • Task-Level Config (Retries, Timeouts)

  • Observability & Alerting Setup

Quality bar

  • Is the DAG idempotent for any given execution date?
  • Are there clear boundaries between extraction, transformation, and loading?
airflow
orchestration
data-engineering
python
intermediate