Description

Introduction

Apache Airflow is one of the most popular open-source tools for orchestrating complex workflows, including data pipelines. It provides a rich set of features for managing and scheduling tasks, integrating various data processing tools, and ensuring that pipelines run smoothly, reliably, and at scale. Airflow allows data engineers to automate and monitor data workflows, making it an essential tool for any organization working with large datasets and complex processes.

In this course, you will learn how to build, schedule, and monitor data pipelines using Apache Airflow. You will explore its key concepts, such as Directed Acyclic Graphs (DAGs), task scheduling, and dependencies, and apply these to real-world data pipeline scenarios. By the end of the course, you will have hands-on experience designing data workflows, handling errors, and optimizing pipelines for scalability and efficiency.

Prerequisites

• Basic knowledge of Python programming.
• Familiarity with data engineering concepts and databases.
• Experience with SQL and data processing tools.
• Understanding of cloud environments and APIs (optional but helpful).

Table of Contents

1. Introduction to Apache Airflow
   1.1 What is Apache Airflow?
   1.2 Key Features of Apache Airflow
   1.3 Benefits of Using Apache Airflow for Data Pipelines
   1.4 Airflow Architecture: Scheduler, Worker, and Web UI
2. Setting Up Apache Airflow
   2.1 Installing and Configuring Apache Airflow
   2.2 Setting Up Airflow on Local and Cloud Environments
   2.3 Understanding Airflow’s File System and Directory Structure
   2.4 Airflow Configuration: DAGs, Executors, and Connections
3. Core Concepts of Apache Airflow
   3.1 Understanding Directed Acyclic Graphs (DAGs)
   3.2 Tasks and Operators: Defining Your Pipeline’s Steps
   3.3 Task Dependencies and Execution Order
   3.4 Airflow’s Task States and Retries
   3.5 Using Airflow Variables and XComs for Data Passing
4. Building Your First Data Pipeline
   4.1 Creating a Simple DAG in Airflow
   4.2 Using Built-in Operators for Data Extraction, Transformation, and Loading (ETL)
   4.3 Scheduling and Triggering DAGs
   4.4 Monitoring DAGs Using the Airflow Web UI
   4.5 Logging and Debugging Airflow Tasks
5. Advanced Operators and Task Management
   5.1 Using PythonOperator, BashOperator, and Custom Operators
   5.2 Handling Task Failures and Retries
   5.3 Dynamic DAGs and Parameterized Pipelines
   5.4 Working with External Triggers and Sensors
   5.5 Managing Task Dependencies with SubDAGs
6. Working with External Data Sources and APIs
   6.1 Integrating with Databases: PostgreSQL, MySQL, and MongoDB
   6.2 Interacting with REST APIs Using HTTP and REST Operators
   6.3 Connecting to Cloud Services (AWS, GCP, Azure)
   6.4 Using Airflow for Data Ingestion from External Systems
7. Optimizing and Scaling Data Pipelines
   7.1 Managing Resources: Parallelism and Concurrency
   7.2 Setting up Task Queues and Priorities for Scalability
   7.3 Optimizing Task Execution Time and Efficiency
   7.4 Leveraging Airflow’s Distributed Executors for Large-Scale Pipelines
   7.5 Optimizing Task Execution with Dynamic Task Mapping
8. Data Pipeline Monitoring and Error Handling
   8.1 Monitoring and Alerting with Airflow
   8.2 Setting Up Email and Slack Notifications for Failures and Successes
   8.3 Understanding Airflow’s Logs and Debugging Tools
   8.4 Error Handling and Managing Task Retries
   8.5 Keeping Track of Task Execution History
9. Advanced Features in Apache Airflow
   9.1 Using Airflow’s REST API for External Control
   9.2 Implementing Custom Operators and Hooks
   9.3 Airflow Plugins: Extending Functionality
   9.4 Data Pipeline Versioning and Reproducibility
   9.5 Securing Airflow with Authentication and Authorization
10. Real-World Use Cases and Best Practices
    10.1 Building a Complex ETL Pipeline with Apache Airflow
    10.2 Automating Data Pipelines in Cloud Environments
    10.3 Best Practices for Scaling and Optimizing Pipelines
    10.4 Managing Data Pipeline Failure Recovery
    10.5 Case Study: Using Apache Airflow in Real-Time Data Processing

Conclusion

By completing this course, you will have a thorough understanding of how to build, manage, and optimize data pipelines using Apache Airflow. You will be proficient in creating DAGs, managing task dependencies, and working with different operators for ETL tasks. Additionally, you will learn how to monitor, scale, and troubleshoot your data pipelines efficiently.

Apache Airflow is a powerful tool for automating workflows and ensuring that complex data engineering tasks are executed reliably. By leveraging its flexibility and scalability, you will be able to build robust data pipelines that integrate with a variety of data sources, handle errors gracefully, and support large-scale, high-performance processing. This course will empower you to streamline your data workflows and apply Airflow in diverse data engineering projects.