Airflow for Workflow Orchestration

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1. What is a Workflow?

• A sequence of tasks to reach a goal ( Trigger , Actions )

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2. The Data Engineer's Role

• We build Data Pipelines (ETL/ELT).

• These pipelines are workflows.

• We use Airflow to automate our pipeline.

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3. Common Workflow Problems

• Dependencies: Task B needs Task A to finish.

• Retries: Handling temporary failures automatically.

• Scheduling: To automate runs based on time intervals.

• Backfilling: Re-running historical data

• Monitoring: Seeing logs and failure points.

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4. Tool

• n8n: Automation (Low-code/Daily tasks).

• Jenkins: DevOps (Testing & Deployment).

• SSIS: Legacy ETL (GUI-based).

• Airflow: Data Orchestration (Python-based).

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5. Why Airflow?

• Deep Integration: Connects with AWS, GCP, Snowflake, Spark, etc.

• Python-Based: Pipelines are managed as code (Git-friendly).

• Open Source: Large community + rich plugin ecosystem

• Scalability: Flexible enough for any project size

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Airflow 2 vs Airflow 3

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