Apache Airflow Fundamentals

Course Duration: 3 Days

Course Description:

This intensive 3-day course provides a comprehensive introduction to Apache Airflow, a platform for programmatically authoring, scheduling, and monitoring workflows. You will learn the core concepts of Airflow, how to design and build data pipelines, and how to effectively manage and monitor your workflows.

Target Audience:

• Data Engineers

• Data Scientists

• DevOps Engineers

• Software Engineers

• Anyone involved in data pipelines and workflow orchestration

Course Objectives:

Upon successful completion of this course, participants will be able to:

• Understand the core concepts of Apache Airflow: DAGs, Tasks, Operators, and the Scheduler.

• Design and implement basic and complex workflows using Airflow.

• Utilize core operators (BashOperator, PythonOperator, etc.) to execute tasks.

• Schedule and trigger workflows using various scheduling mechanisms.

• Handle task dependencies and control workflow execution.

• Monitor workflow progress, troubleshoot issues, and handle failures.

• Utilize XComs for data sharing between tasks.

• Understand and implement basic Airflow security and administration.

Course Outline:

Day 1: Airflow Fundamentals & Core Concepts

• Introduction to Apache Airflow:

  • What is Airflow?

  • Core Concepts: DAGs, Tasks, Operators, Scheduler, Executor

  • Airflow Architecture and Components

  • Installation and Setup (Local or Cloud)

• Hands-on:

  • Creating a simple DAG with basic operators (BashOperator, PythonOperator)

  • Scheduling and running a DAG

  • Monitoring DAG runs and task instances in the Airflow UI

Day 2: Advanced Concepts & Data Flow

• Data Flow and Dependencies:

  • Task dependencies and their impact on workflow execution

  • Using XComs to pass data between tasks

  • Branching and merging workflows

• Operators & Connectors:

  • Exploring common operators (e.g., HTTPOperator, EmailOperator, S3Hook, GoogleCloudHook)

  • Connecting Airflow to external systems (databases, cloud services)

  • Hands-on: Building a more complex workflow with data dependencies and external system interactions

Day 3: Scheduling, Monitoring, and Best Practices

• Scheduling and Triggering:

  • Scheduling options (cron expressions, intervals)

  • Trigger rules and dependencies

  • Backfilling and catching up past schedules

• Monitoring and Troubleshooting:

  • Monitoring DAG runs and task instances

  • Handling task failures and retries

  • Debugging and troubleshooting workflows

• Best Practices:

  • Designing maintainable and scalable workflows

  • Security considerations in Airflow

  • Best practices for managing and deploying Airflow

  • Q&A and Wrap-up