Introduction

In the era of big data and cloud computing, data engineers need robust, scalable, and automated workflows to manage data pipelines efficiently. Apache Airflow has become the industry standard for orchestrating complex data workflows, offering dynamic scheduling, monitoring, and dependency management for data pipelines.

This course provides an in-depth, hands-on approach to building, managing, and optimizing data workflows with Apache Airflow. Participants will learn how to design scalable ETL pipelines, integrate Airflow with cloud services (AWS, GCP, Azure), and optimize workflow performance for modern data-driven applications.

Course Objectives

By the end of this course, participants will be able to:

• Understand Apache Airflow architecture and core concepts.
• Build dynamic, scalable, and maintainable DAGs (Directed Acyclic Graphs) for workflow automation.
• Use Airflow Operators, Sensors, and Hooks for connecting to databases, APIs, and cloud services.
• Deploy ETL pipelines using Airflow for batch and streaming data.
• Integrate Airflow with big data tools like Apache Spark, Snowflake, and Kubernetes.
• Set up Airflow on-premises or in the cloud (AWS, GCP, Azure) for production workloads.
• Monitor, troubleshoot, and optimize Airflow workflows for performance and reliability.

Who Should Attend?

This course is ideal for:

• Data engineers designing scalable data pipelines.
• Data scientists automating machine learning workflows.
• Cloud engineers and architects working with Airflow-based deployments.
• Software engineers managing data workflow automation.
• DevOps professionals deploying and monitoring data pipeline infrastructure.

Day-by-Day Course Breakdown

Day 1: Introduction to Apache Airflow & Workflow Orchestration

Understanding Workflow Orchestration & Data Pipelines

• What is workflow orchestration?
• Why use Apache Airflow for data engineering?
• Comparison with other workflow tools: Prefect, Luigi, Kubeflow

Apache Airflow Architecture & Core Components

• DAGs (Directed Acyclic Graphs) and Task Scheduling
• Airflow Components: Scheduler, Executor, Metadata Database, Web UI
• Installing and configuring Apache Airflow (local & cloud environments)

Building Your First DAG

• Writing DAGs in Python
• Configuring task dependencies and retries
• Running and monitoring workflows in the Airflow UI
• Hands-on lab: Creating and executing a simple DAG

Day 2: DAG Development & Task Management

Advanced DAG Design

• Dynamic DAG generation with Python
• Using the TaskFlow API for modular workflows
• Managing dependencies and task execution order

Working with Airflow Operators, Sensors, and Hooks

• Using built-in operators (PythonOperator, BashOperator, EmailOperator)
• Implementing Sensors for event-driven workflows
• Connecting to databases and cloud storage using Hooks

ETL Pipelines with Apache Airflow

• Designing and implementing Extract, Transform, Load (ETL) workflows
• Data ingestion from REST APIs, databases, and cloud storage
• Hands-on lab: Building an ETL pipeline with Airflow

Day 3: Integrating Airflow with Big Data & Cloud Services

Airflow with Cloud Services (AWS, GCP, Azure)

• Deploying Airflow on AWS Managed Workflows for Apache Airflow (MWAA)
• Using Google Cloud Composer for Airflow orchestration
• Running Airflow in Azure with Kubernetes-based setups

Airflow & Big Data Ecosystem

• Integrating Apache Spark, Snowflake, and Databricks with Airflow
• Managing machine learning pipelines with Airflow and TensorFlow
• Hands-on lab: Orchestrating a Spark job with Airflow

Deploying Airflow in a Production Environment

• Airflow Executors: Local, Celery, Kubernetes
• Configuring and managing Airflow in a distributed environment
• Hands-on lab: Deploying Airflow on Kubernetes

Day 4: Monitoring, Scaling, and Optimizing Airflow Workflows

Logging, Monitoring, and Alerts in Airflow

• Tracking DAG execution logs and debugging failures
• Setting up alerting with Slack, PagerDuty, and email notifications
• Using Prometheus and Grafana for Airflow monitoring

Performance Tuning & Scaling Airflow

• Best practices for DAG optimization and efficient scheduling
• Managing large-scale workflows with parallel execution
• Handling failures, retries, and state management in Airflow

Security & Access Control in Airflow

• Role-based access control (RBAC) for managing permissions
• Securing connections and sensitive credentials in Airflow
• Hands-on lab: Implementing monitoring and alerts in an Airflow DAG

Day 5: Capstone Project & Future Trends in Workflow Orchestration

Future of Workflow Orchestration

• Airflow 2.x updates and roadmap
• The rise of serverless data pipelines and cloud-native orchestration
• Introduction to Prefect, Dagster, and Kubeflow Pipelines

Capstone Project: End-to-End Data Pipeline with Apache Airflow

• Participants will design, implement, and deploy a real-world data pipeline
• Integrate data ingestion, transformation, and machine learning workflows
• Present findings and workflow optimizations

Conclusion & Certification

At the end of the training, participants will receive a Certificate of Completion, validating their expertise in Apache Airflow for data engineering.

This course combines theory, practical labs, real-world use cases, and best practices to prepare participants for future challenges in workflow automation and data pipeline orchestration.