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Deadline to Die

Apache Airflow Deep Dive

🎼 Apache Airflow Deep Dive

Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. It uses Python to define DAGs (Directed Acyclic Graphs).

🟢 Level 1: Foundations (The Core Concepts)

1. The DAG (Directed Acyclic Graph)

A DAG is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies.

  • Directed: Has a specific flow from start to end.
  • Acyclic: Cannot have loops (Task A cannot depend on Task B if Task B depends on Task A).

2. Operators & Tasks

  • Operators: The templates for a task (e.g., PythonOperator, BashOperator, S3ToSnowflakeOperator).
  • Tasks: A specific instance of an operator inside a DAG.

🟡 Level 2: The Architecture

Airflow consists of several components:

  • Web Server: The UI for monitoring and managing DAGs.
  • Scheduler: The “Brain” that triggers tasks when dependencies are met.
  • Executor: Handles running the tasks (e.g., CeleryExecutor for distributed workers, KubernetesExecutor).
  • Metadata Database: Stores state, logs, and user information (usually Postgres).

🔴 Level 3: Advanced Orchestration

3. Dynamic DAG Generation

Instead of hardcoding 100 DAGs, use Python to generate them dynamically from a configuration file or a database.

4. XComs (Inter-Task Communication)

Allows tasks to exchange small amounts of metadata (like a file path or a record count).

5. Task Groups & SubDAGs

Organize complex DAGs with hundreds of tasks into manageable visual groups in the UI.

Keep your Airflow tasks Idempotent. If a task fails and is retried, it should not create duplicate data. Always use “Overwrite” or “Upsert” logic in your destination storage.