The Complete Hands-On Introduction to Apache Airflow 3

Apache Airflow is a powerful, open-source platform for programmatically authoring, scheduling, and monitoring workflows. If you're overwhelmed with ETL pipelines and need a robust solution, Airflow is essential.

This comprehensive course will guide you through everything you need to master Apache Airflow 3, combining clear theory with practical video demonstrations.

You'll begin with a solid foundation:

• Understanding Apache Airflow: Learn the fundamental principles and benefits of using Airflow for workflow management.
• Core Airflow Concepts: Explore the key components and terminology that underpin Airflow's functionality.
• Airflow Architectures: Discover different deployment options to suit your infrastructure needs.
• Workflow Execution: Understand the process of how workflows are triggered and executed within Airflow.

Then, you'll build a complete data pipeline from the ground up, incorporating essential Airflow features:

• Sensors: Implement strategies to wait for specific conditions before proceeding with tasks.
• Hooks: Learn how to seamlessly interact with various systems, including databases, APIs, and cloud services.
• TaskFlow: Master writing efficient and readable Directed Acyclic Graphs (DAGs) for complex workflows.
• XCOMs: Discover how to securely share data between tasks within your DAGs.
• And much more!

By the end of this project, you'll be fully equipped to design and implement your own Airflow workflows.

Beyond the core concepts, you'll delve into the exciting new Asset syntax introduced in Airflow 3. Learn:

• What are Assets? Understand the concept of Assets and their role in Airflow.
• Asset Dependencies: Learn how to define and manage dependencies between Assets.
• Asset Materialization: Discover techniques for materializing Assets for improved performance.

Scale your Airflow deployments with advanced executor options. We'll focus on the highly popular CeleryExecutor:

• CeleryExecutor Configuration: Learn how to configure Airflow to leverage the power of Celery.
• Task Distribution: Distribute tasks across multiple workers for parallel processing.
• Worker Queues: Choose the optimal worker configuration using queues for efficient resource allocation.

Finally, elevate your DAGs with advanced features and conclude by building your own custom Airflow provider and a new decorator for executing SQL requests. This is particularly valuable for those working within organizations already utilizing Airflow.

Get ready to unlock the full potential of Apache Airflow and streamline your data workflows!