• Duration

    3 Hours

  • Level

    Intermediate

  • Course Type

    Free Course

What you'll Learn

  • Understand Airflow Concepts & Architecture Gain a solid foundation in DAGs, tasks, operators, and the architecture of Apache Airflow.

  • Build & Schedule ETL Workflows Learn to create, manage, and schedule DAGs using real-world projects and cron expressions.

  • Apply Best Practices for Workflow Automation: Implement branching, task dependencies, hooks, and clean coding techniques for scalable workflow orchestration.

About the Instructor

Kunal Jain - Founder, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.
About the Instructor

Who Should Enroll

  • Ideal for students aiming to build a career in data engineering or workflow orchestration by gaining hands-on experience with real-world ETL pipeline projects.

  • Perfect for data engineers, analysts, or developers looking to automate and scale their data workflows using Apache Airflow in production environments.

Course curriculum

  • 1
    Introduction to the Course
    • Case Study: Story of Airflow
    • Course Outline
    • Prerequisites
    • Course Handouts
  • 2
    Introduction to Apache Airflow
  • 3
    Installation Steps
  • 4
    Getting started with airflow
  • 5
    Exploring features of airflow
  • 6
    Project Implementation
  • 7
    Task dependencies
  • 8
    Scheduling in Depth
  • 9
    Best Practices

FAQ

  • What is Apache Airflow, and why is it used?

    Apache Airflow is an open-source tool used for programmatically authoring, scheduling, and monitoring data workflows. It’s ideal for managing complex ETL pipelines.

  • What are DAGs, and how do they relate to workflows?

    DAGs (Directed Acyclic Graphs) represent workflows as a series of tasks with defined execution order. They are the backbone of Airflow scheduling.

  • How is Airflow different from traditional ETL tools?

    Airflow is dynamic, code-first, and scalable—allowing for better flexibility, reusability, and monitoring compared to rigid GUI-based ETL tools.

  • What are some common types of Airflow Operators?

    PythonOperator, BashOperator, DummyOperator, EmailOperator, and custom operators created for specific use cases.

  • Will I receive a certificate upon completing the course?

    Yes, the course provides a certification upon completion.