Overview

Apache Airflow Best Practices equips data professionals with the skills to master Airflow, from foundational concepts to advanced deployment strategies. This course is essential for those wanting to build scalable data pipelines, optimize workflows, and leverage Airflow in cloud environments. Through practical demonstrations and real-world examples, the course will guide you in creating efficient, optimized workflows, enhancing your data engineering capabilities. You'll gain hands-on experience in managing complex workflows and automating data tasks. What sets this course apart is its unique combination of theoretical knowledge and hands-on exercises, ensuring you can apply learned concepts in real-world settings, giving you the confidence to tackle real challenges. This course is ideal for data engineers, developers, and data scientists looking to improve their workflow orchestration skills. No prior Airflow experience is required, though basic knowledge of Python and DevOps concepts is recommended.

Syllabus

Getting Started with Airflow 2.0

In this section, we explore data orchestration fundamentals, Airflow 2.0 features, and best practices for building scalable pipeline solutions.

Core Airflow Concepts

In this section, we explore Apache Airflow's core concepts, including DAGs, task groups, and triggers, and how to implement them for efficient workflow automation and optimization.

Components of Airflow

In this section, we explore Airflow components, their roles, and how to select and optimize executors for efficient workflow orchestration and scalability.

Basics of Airflow and DAG Authoring

In this section, we explore Airflow DAG authoring for API data extraction, focusing on task design with operators and workflow optimization for efficient data pipelines.

Connecting to External Sources

In this section, we explore connecting Apache Airflow to external sources, designing DAGs with failure alerts, and managing secrets securely for efficient workflow automation.

Extending Functionality with UI Plugins

In this section, we cover creating Airflow UI plugins for custom workflow monitoring using Flask blueprints and metrics dashboards.

Writing and Distributing Custom Providers

In this section, we explore creating and distributing custom Airflow providers, focusing on structured packaging, testing, and reusable code for scalable workflow automation.

Orchestrating a Machine Learning Workflow

In this section, we explore orchestrating machine learning workflows, focusing on DAG design, implementation, and MLOps practices for operational model deployment and performance analysis.

Using Airflow as a Driving Service

In this section, we explore abstracting Airflow workflows to enable non-technical users to create and manage tasks. Key concepts include templated DAGs, workflow scheduling, and simplified orchestration for improved collaboration.

Airflow Ops Development and Deployment

In this section, we explore Airflow deployment strategies, DAG delivery patterns, and secure configuration management to optimize workflow efficiency and reliability.

Airflow Ops Best Practices Observation and Monitoring

In this section, we explore monitoring strategies for Airflow systems and DAGs, focusing on core component health, DAG performance metrics, and alerting mechanisms for efficient workflow management.

Multi-Tenancy in Airflow

In this section, we explore strategies for implementing multi-tenancy in Airflow, focusing on isolation, operational requirements, and secure shared infrastructure management.

Migrating Airflow

In this section, we explore planning migration activities, implementing technical strategies, and executing pipeline changes with minimal downtime to ensure smooth Airflow transitions.