Draft content

Airflow Data Quality Workflow

Draft case study for an orchestrated workflow that separates extraction, transformation, validation, and publish steps.

AirflowPythonSQLDockerPostgreSQL

Problem

Data pipelines need scheduling, task dependencies, failure visibility, and quality checks before downstream users rely on output tables.

Data Sources

API extracts
Warehouse tables
Validation rules
Pipeline run metadata

Architecture

Scheduled DAG -> extraction task -> staging load -> transformations -> data quality checks -> publish task -> notification.

Pipeline Design

The workflow uses separate tasks for extraction, loading, transformation, and validation. Quality checks run before the publish step so failed checks can stop downstream tables from being refreshed incorrectly.

Data Model

raw_api_events
stg_api_events
dq_check_results
mart_valid_events

Data Quality Checks

Freshness checks
Schema validation
Null-rate thresholds
Duplicate detection
Failed-check logging

Challenges

The main challenge is making failures visible and actionable. Validation output should identify the failed rule, affected table, run time, and severity.

Outcome

This project demonstrates a maintainable orchestration pattern for reliable batch workflows. Replace this draft with a real DAG screenshot, failure handling notes, and repository links.