What you'll Learn
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Understand Airflow Concepts & Architecture Gain a solid foundation in DAGs, tasks, operators, and the architecture of Apache Airflow.
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Build & Schedule ETL Workflows Learn to create, manage, and schedule DAGs using real-world projects and cron expressions.
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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.
Who Should Enroll
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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.
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Perfect for data engineers, analysts, or developers looking to automate and scale their data workflows using Apache Airflow in production environments.
Course curriculum
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1
Introduction to the Course
- Case Study: Story of Airflow
- Course Outline
- Prerequisites
- Course Handouts
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2
Introduction to Apache Airflow
- What Is Airflow
- Quiz : What Is Airflow
- Airflow Architecture
- Quiz : Airflow Architecture
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3
Installation Steps
- Airflow Linux Installation
- Airflow Windows Installation
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4
Getting started with airflow
- What Are Dags
- Quiz : What Are Dags
- Tasks Vs Operators
- Quiz : Tasks Vs Operators
- Components Of Airflow Ui
- Quiz : Components Of Airflow Ui
- Building Your First Dag Bashoperator
- Building Your First Dag - PythonOperator
- Quiz : Building your first dag - PythonOperator
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5
Exploring features of airflow
- Problem Statement
- Fetching Candidate Data
- Project Dag Api Call Script-1
- Project Dag Api Call-1
- Understanding Cron Expressions
- Project Dag Scheduled Api Call-1
- Project Dag Api Call Retry-1
- Quiz : Cron Expressions - Project Dag API call
- Timeout
- Project Dag Api Call Timeout-1
- Quiz : Project Dag - API Call Timeout
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6
Project Implementation
- Project Candidate Screening
- Dag Candidate Screening Script
- Dag Candidate Screening
- Project Interview Scheduling Onboarding
- Dag Interview Scheduling Onboarding Overview
- Dag Schedule Interview Script
- Dag Candidate Feedback Script-1
- Dag Candidate Onboarding Script-1
- Dag Interview Scheduling-1
- Airflow Hooks
- Dag S3 Hook
- Quiz : Creating Custom Operator
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7
Task dependencies
- Task Dependencies
- Quiz : Task Dependencies
- What Is Branching
- Quiz : What Is Branching
- Project Branching Interviewer Data
- Dag Branching-Interviewer Data
- Sharing Data Between Tasks
- Quiz : Sharing Data Between Tasks
- Dag Conditional Task For Api Call
- Quiz : Creating Custom Hook
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8
Scheduling in Depth
- Process Data Incrementally
- Dag Hr Reporting
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9
Best Practices
- Writing Clean And Reproducible Tasks
- Quiz : Writing Clean and reproducible Tasks
- Further Possibilities In Project
FAQ
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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.
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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.
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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.
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What are some common types of Airflow Operators?
PythonOperator, BashOperator, DummyOperator, EmailOperator, and custom operators created for specific use cases.
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Will I receive a certificate upon completing the course?
Yes, the course provides a certification upon completion.