By completing this course, learners will:

• Write and manage DAGs in Airflow.

• Use operators, hooks, and sensors effectively.

• Orchestrate ETL, ML, and data workflows.

• Deploy Airflow in cloud and containerized environments.

• Monitor and troubleshoot pipelines in production.

Course Syllabus

Module 1: Introduction to Airflow

• What is Apache Airflow?

• Airflow architecture overview

• Airflow vs. other orchestration tools (Luigi, Prefect, Dagster)

Module 2: Getting Started

• Installing Airflow (local, Docker, cloud)

• Airflow CLI and UI overview

• Creating your first DAG

Module 3: DAGs & Scheduling

• DAG concepts and structure

• Scheduling workflows (cron, interval)

• Backfilling and retries

Module 4: Operators & Tasks

• Core operators (Python, Bash, Email, etc.)

• Custom operators

• Sensors and hooks for integrations

Module 5: Workflow Orchestration

• Task dependencies and branching

• Parallelism and task concurrency

• XComs and inter-task communication

Module 6: Data Pipelines

• Building ETL workflows

• Integrating with databases (Postgres, MySQL)

• Cloud storage (S3, GCS, Azure Blob)

Module 7: Machine Learning Pipelines

• Orchestrating ML training workflows

• Model deployment pipelines

• Experiment tracking with Airflow

Module 8: Deployment & Scaling

• Airflow with Docker Compose

• Running Airflow on Kubernetes

• Managed Airflow on AWS, GCP, and Azure

Module 9: Monitoring & Logging

• Airflow logs and metrics

• Alerts and notifications

• Debugging workflows

Module 10: Security & Governance

• Role-based access control (RBAC)

• Authentication and authorization

• Compliance and audit trails

Module 11: Advanced Features

• Dynamic DAGs

• Plugins and extensions

• Airflow REST API

Module 12: Real-World Projects

• Data warehouse ETL pipeline (Snowflake/BigQuery)

• ML training pipeline with Airflow + TensorFlow

• Analytics pipeline with Airflow + Spark

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in Apache Airflow and workflow orchestration. This certificate demonstrates readiness for roles in data engineering, ML engineering, and MLOps.

Apache Airflow skills prepare learners for roles such as:

• Data Engineer

• MLOps Engineer

• Data Scientist (Pipeline Automation)

• Cloud Data Engineer

• Workflow Orchestration Specialist

Airflow has become the de facto standard for data pipeline orchestration, making it a must-have skill for data-driven organizations.

1. What is Apache Airflow?
   An open-source tool for workflow orchestration, managing data pipelines with DAGs.

2. What is a DAG in Airflow?
   A Directed Acyclic Graph that defines the structure of workflows.

3. What are operators in Airflow?
   Pre-built classes that define specific tasks (e.g., PythonOperator, BashOperator, SQLOperator).

4. How does Airflow schedule workflows?
   Using cron-like expressions or presets to trigger DAG runs.

5. What are sensors in Airflow?
   Tasks that wait for external events before proceeding.

6. What is XCom in Airflow?
   A mechanism for sharing small pieces of data between tasks.

7. How does Airflow scale?
   By using executors like CeleryExecutor or KubernetesExecutor.

8. What are Airflow use cases?
   ETL, ML pipelines, cloud integration, and data warehousing.

9. How do you deploy Airflow in production?
   With Docker, Kubernetes, or managed services like Astronomer/Cloud Composer.

10. What are common challenges in Airflow?
    Scaling large DAGs, handling dependencies, and monitoring failures.