Build Data Pipelines with Apache Airflow

This course provides a practical introduction to Apache Airflow, one of the most popular workflow orchestration tools used in modern data engineering. Learners will understand how data pipelines are designed, scheduled, monitored, and automated using Airflow. The course covers workflow orchestration concepts, Directed Acyclic Graphs (DAGs), task dependencies, scheduling mechanisms, operators, sensors, and real-world pipeline development. Through hands-on examples and case studies, students will learn how organizations automate data movement, data processing, and ETL workflows to build reliable and scalable data engineering solutions.

Get an overview of the complete course structure, learning objectives, and the practical skills you will gain while building data pipelines using Apache Airflow.

Understand the fundamentals of Apache Airflow, its purpose, and how it helps automate, schedule, and monitor complex data workflows. Learn why Airflow has become one of the most popular workflow orchestration tools in modern data engineering.

Explore the internal architecture of Apache Airflow and understand how its core components work together to schedule, execute, and monitor workflows efficiently.

Learn how to install and configure Apache Airflow on a Linux operating system. This lesson covers the required prerequisites, environment setup, package installation, database initialization, and verification steps needed to run Airflow successfully.

Understand how to install Apache Airflow on Windows and configure the necessary tools and dependencies required to run Airflow in a Windows environment.

Learn the fundamental concept of Directed Acyclic Graphs (DAGs), the core building block of Apache Airflow. Understand how DAGs define workflow structures, task dependencies, and execution order.

Understand the difference between tasks and operators in Apache Airflow and learn how operators define actions while tasks represent operator instances within a workflow.

Explore the Apache Airflow User Interface and learn how to monitor, manage, and troubleshoot workflows using its various components and visualization tools.

Learn how to create your first Apache Airflow workflow using BashOperator to execute shell commands and automate simple system-level tasks.

Learn how to create Airflow workflows using PythonOperator and execute custom Python functions as part of automated data pipelines.

Understand the real-world business problem that will be solved throughout this section and learn how Apache Airflow can automate repetitive data collection and processing workflows.

Learn how candidate information can be retrieved from external sources and prepared for automated processing within an Airflow workflow

Learn how to develop the Python script responsible for making API requests and retrieving data for the Airflow project workflow.

Integrate the API call script into an Airflow DAG and automate data retrieval as part of a workflow.

Learn how cron expressions are used in Apache Airflow to define workflow schedules and automate task execution at specific intervals.

Learn how to automate API-based workflows by applying scheduling configurations to Airflow DAGs.

Learn how to configure retry mechanisms in Apache Airflow to improve workflow reliability and handle temporary failures automatically.

Learn how task timeout settings work in Apache Airflow and understand how they help prevent workflows from hanging indefinitely due to long-running or unresponsive tasks.

Apply timeout configurations to an API-based Airflow workflow and learn how Airflow handles tasks that exceed their execution limits.

Understand the candidate screening process in a recruitment pipeline and learn how Apache Airflow can automate candidate evaluation, filtering, and decision-making tasks based on predefined criteria.

Learn how to develop the Python script that performs candidate screening operations and prepares the business logic for integration into an Airflow DAG.

Integrate the candidate screening script into Apache Airflow and automate the screening process using a fully functional DAG.

Learn how Apache Airflow can automate interview scheduling and onboarding processes for candidates who successfully pass the screening stage.

Gain a comprehensive understanding of the DAG structure used to automate interview scheduling and onboarding activities.

Develop the Python script responsible for automating interview scheduling and integrating scheduling logic into the Airflow workflow.

Learn how to create a Python script that collects and processes candidate feedback after interviews and prepares the information for workflow automation within Apache Airflow.

Learn how to create the onboarding automation script that manages post-selection activities and prepares successful candidates for joining the organization.

Integrate interview scheduling functionality into an Apache Airflow DAG and automate the interview coordination process.

Explore Airflow Hooks and learn how they simplify connections to external systems, databases, cloud platforms, and APIs.

Learn how to use Amazon S3 Hooks within Apache Airflow to interact with cloud storage services and automate file management tasks.

Learn how task dependencies work in Apache Airflow and understand how they control the execution order of tasks within a workflow. This lesson demonstrates how dependencies ensure that tasks run in the correct sequence and only after prerequisite tasks have completed successfully.

Learn the concept of branching in Apache Airflow and understand how workflows can follow different execution paths based on conditions, decisions, or task outcomes.

Explore a real-world branching scenario involving interviewer data and learn how decision-based workflows can be applied to recruitment processes.

Implement branching logic within an Apache Airflow DAG and automate interviewer assignment workflows based on candidate and interviewer information.

Learn how tasks communicate with one another in Apache Airflow by sharing data, enabling workflows to pass information between different stages of execution.

Learn how to implement conditional task execution for API calls in Apache Airflow, allowing workflows to make decisions based on API responses and execute tasks dynamically.

Learn the concept of incremental data processing and understand how Apache Airflow can efficiently process only new or updated data instead of reprocessing entire datasets during every workflow execution

Learn how to build and schedule an HR reporting workflow in Apache Airflow that automatically generates and delivers periodic reports based on organizational data.

Learn the best practices for writing clean, maintainable, and reproducible Airflow tasks that can be easily understood, tested, and reused across different workflows and environments

Explore advanced enhancements and future improvements that can be added to the recruitment automation project using Apache Airflow and related technologies.