This Apache Airflow training is designed to help participants design, schedule, monitor, and manage complex data and workflow pipelines using Apache Airflow. The course covers Airflow architecture, DAGs, operators, scheduling, dependencies, monitoring, error handling, and best practices for building reliable workflows. Participants will gain hands-on experience to orchestrate data pipelines and automate workflows in modern data engineering and DevOps environments.

• Understand the architecture, components, and workflow orchestration capabilities of Apache Airflow.
• Install, configure, and manage Apache Airflow environments for data pipeline automation.
• Design, schedule, and manage workflows using DAGs, tasks, operators, and scheduling mechanisms.
• Integrate Airflow with databases, cloud services, APIs, and data processing platforms.
• Monitor workflow execution, troubleshoot failures, and optimize pipeline performance and reliability.
• Build scalable, secure, and production-ready workflow automation solutions using Apache Airflow best practices.

 • Data engineers
 • Data analysts and BI engineers
 • DevOps and platform engineers
 • Backend developers
 • Professionals managing data pipelines and workflows

 • Basic understanding of Python programming
 • Familiarity with data pipelines or ETL concepts is helpful
 • Interest in workflow orchestration and automation

 • Understanding Apache Airflow architecture
 • Creating and managing DAGs
 • Using operators and sensors
 • Managing task dependencies
 • Scheduling and monitoring workflows
 • Handling failures and retries
 • Managing Airflow environments
 • Applying workflow orchestration best practices

By the end of this training, participants will be able to design, deploy, and manage reliable workflows using Apache Airflow. Learners will gain strong fundamentals in workflow orchestration, enabling them to automate data pipelines and operational tasks at scale.

Introduction to Workflow Orchestration & Apache Airflow
 • What is workflow orchestration
 • Apache Airflow use cases
 • Airflow architecture and components
 • Airflow concepts and terminology

Airflow Installation & Environment Setup
 • Airflow installation overview
 • Airflow configuration basics
 • Web UI overview
 • Understanding metadata database

DAG Fundamentals
 • What is a DAG
 • DAG structure and syntax
 • Scheduling concepts
 • Defining task dependencies

Operators, Tasks & Sensors
 • Common operators overview
 • Bash and Python operators
 • Sensors and their use cases
 • Task lifecycle

Advanced DAG Design & Scheduling
 • Dynamic DAGs
 • Branching and conditional workflows
 • SubDAG concepts
 • Best practices for DAG design

Connections, Variables & Secrets Management
 • Managing connections
 • Using Airflow variables
 • Handling secrets securely
 • Environment configuration

Monitoring, Logging & Alerts
 • Monitoring DAG execution
 • Task logs and retries
 • Email and alerting setup
 • Troubleshooting failed workflows

Error Handling & Reliability
 • Retry strategies
 • SLA management
 • Backfilling and catchup
 • Handling data dependencies

Airflow Executors & Scaling Concepts
 • Local vs Celery executors
 • Kubernetes executor overview
 • Scaling Airflow deployments
 • Performance considerations

Integration with Data Platforms & Tools
 • Integrating with databases
 • Working with cloud storage
 • API-based workflows
 • ETL and ELT orchestration patterns

Security & Access Control Basics
 • Authentication and authorization
 • Role-based access control
 • Securing Airflow UI
 • Best practices for production setups

Airflow in Production & Best Practices
 • Deployment strategies
 • Version control for DAGs
 • CI/CD for Airflow
 • Operational best practices

Apache Airflow Capstone Workshop & Best Practices
 • Building an end-to-end data pipeline DAG
 • Scheduling and monitoring workflows
 • Handling failures and retries
 • Final workshop review and best practices

• Apache Airflow Setup & Workflow Orchestration Architecture
• DAG Development, Task Scheduling & Operator Configuration
• Data Pipeline Integration & Workflow Automation
• Monitoring, Troubleshooting & Performance Optimization
• End-to-End Workflow Automation Project

Participants will be evaluated through hands-on Airflow labs, practical DAG development exercises, instructor-led reviews, and a final assessment focused on building and managing a complete Airflow-based workflow.

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Upon successful completion of the training, participants will receive an AcadNXT Certificate of Completion for Apache Airflow. This digital, verifiable certification validates practical Apache Airflow usage, DAG orchestration skills, and workflow automation expertise and can be shared on LinkedIn and included in professional profiles to enhance data engineering and DevOps career credibility.