Apache Airflow – Workflow Orchestration & Scheduling Course in Chennai

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Master Apache Airflow Through Real-Time Workflow Orchestration & Scheduling Projects

Edubrights offers industry-focused Apache Airflow – Workflow Orchestration & Scheduling Training in Chennai designed for students, freshers, data engineers, ETL developers, DevOps engineers, MLOps engineers, cloud professionals, software developers, and working professionals seeking expertise in workflow orchestration, task scheduling, and data pipeline automation. Learn how to design, schedule, monitor, and manage complex workflows and ETL pipelines using Apache Airflow.

Gain practical experience in DAG (Directed Acyclic Graph) development, workflow orchestration, task scheduling, ETL pipeline automation, Python operators, sensors, task dependencies, scheduling strategies, workflow monitoring, logging, alerting, Airflow architecture, cloud deployment, and real-world Apache Airflow implementation projects through expert-led training and hands-on labs.

Why Choose Edubrights for Apache Airflow Training?

✅ Real-Time Workflow Orchestration Projects & Industry Case Studies
✅ Live Instructor-Led Training by Data Engineering Experts
✅ Hands-On Practice with Apache Airflow & Python
✅ ETL Pipeline Automation & Workflow Scheduling Experience
✅ Certification Preparation & Technical Guidance
✅ Resume Building & Mock Interview Preparation
✅ Career Guidance & Job Assistance
✅ Flexible Online, Classroom & Weekend Training Options

Build In-Demand Workflow Orchestration Skills

Learn how leading organizations use Apache Airflow to automate ETL workflows, orchestrate data pipelines, schedule batch processing jobs, monitor workflow execution, and improve operational efficiency across modern data engineering and cloud environments. Our training combines practical implementation, workflow automation concepts, and industry best practices to help you become job-ready.

Start Your Apache Airflow Career Today

Join Edubrights' Apache Airflow – Workflow Orchestration & Scheduling Course in Chennai and gain the skills required to automate workflows, orchestrate enterprise data pipelines, schedule complex tasks, and build a successful career in Data Engineering, DevOps, and Cloud Automation.

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₹38000

₹45000

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Enroll Now View Syllabus

Case Studies and Projects

Hours of Training

20+

Placement Assurance

100%

Expert Support

yes

Support & Access

Lifetime

Certification

Yes

Skill Level

All

Language

All

Course Objectives

1. What will you learn in the Apache Airflow course?

You will learn how to design, schedule, automate, and monitor data pipelines using Apache Airflow. The course covers DAG development, task scheduling, workflow orchestration, operators, sensors, hooks, XComs, error handling, monitoring, and Airflow deployment for modern data engineering environments.

2. How does this course prepare you for real-world data engineering projects?

The course provides hands-on experience in building ETL/ELT pipelines, orchestrating cloud workflows, automating data processing, integrating with databases and APIs, managing dependencies, and monitoring production data workflows.

3. What practical skills will you develop during the training?

You will gain expertise in Apache Airflow architecture, DAG creation, Python-based workflow development, scheduling, task dependencies, operators, sensors, Airflow UI, logging, monitoring, and cloud integration.

4. How will real-time projects improve your Apache Airflow skills?

You will work on practical projects involving enterprise ETL pipelines, cloud data integration, data warehouse automation, API orchestration, batch processing, and workflow monitoring to gain industry-ready experience.

5. What career opportunities can you pursue after learning Apache Airflow?

This course prepares you for roles such as Data Engineer, ETL Developer, Big Data Engineer, Cloud Data Engineer, Data Platform Engineer, Analytics Engineer, DevOps Engineer, and Data Pipeline Developer.

Popular Techniques Covered in This Course

Apache Airflow Installation and Architecture

DAG (Directed Acyclic Graph) Development

Python Programming for Workflow Automation

Operators, Sensors, Hooks, and XComs

Workflow Scheduling and Task Dependencies

ETL and ELT Pipeline Automation

Database and REST API Integration

Workflow Monitoring, Logging, and Alerting

Cloud Integration with AWS, Azure, and GCP

Airflow Deployment, Scaling, and Best Practices

Get Hands-on Knowledge about Real-Time Projects

Project 1

Project 1: Automated ETL Data Pipeline

Develop an ETL workflow that extracts data from multiple sources, transforms it, and loads it into a data warehouse using Apache Airflow.

Project 2

Project 2: Cloud Data Processing Workflow

Build an automated workflow to orchestrate data processing across cloud storage, databases, and analytics platforms using Airflow.

Project 3

Project 3: API Data Integration Pipeline

Create scheduled workflows that retrieve data from REST APIs, validate records, transform datasets, and store them in relational databases.

Project 4

Project 4: Enterprise Data Warehouse Automation

Design Airflow DAGs to automate daily, weekly, and monthly data warehouse refreshes with monitoring, retries, and notifications.

Project 5

Project 5: End-to-End Analytics Workflow

Develop a complete workflow to automate data ingestion, transformation, reporting, and dashboard refresh for business intelligence applications.

Key Features

Edubrights offers Apache Airflow – Workflow Orchestration Training in virtual mode with expert trainers. Here are the key features,

40 Hours Course Duration

Industry Expert Faculties

Free Demo Class Available

Completed 500+ batches

Certification Guidance

Curriculum

Module 1: Airflow Introduction

Workflow orchestration: why scheduling is not enough
Airflow architecture: Scheduler, Executor, Worker, Webserver
DAGs, tasks, and operators: core concepts
Airflow 2.x: TaskFlow API and improvements
Local installation: pip and Docker Compose
Astro CLI for local Airflow development

Module 2: Core Operators

PythonOperator and TaskFlow @task decorator
BashOperator for shell commands
EmailOperator for notifications
BranchPythonOperator for conditional flows
DummyOperator / EmptyOperator for flow control
Task dependencies: set_upstream and set_downstream

Module 3: DAG Design Patterns

Dynamic DAGs: programmatic task generation
SubDAGs vs TaskGroups for modularity
Sensor patterns: FileSensor, ExternalTaskSensor
Trigger rules: all_success, one_failed, none_failed
SLA miss callbacks and task timeouts
DAG parameterisation with dag_run.conf

Module 4: Connections & Hooks

Airflow Connections: UI and environment variable config
PostgreSQL hook: execute and get_pandas_df
S3Hook and GCSHook for cloud storage
HttpHook for REST API calls
SparkSubmitOperator for Spark jobs
Secrets Backend: AWS Secrets Manager, HashiCorp Vault

Module 5: Data Pipeline Patterns

ELT pipeline with Airflow: extract, load, transform
DBT operator for transformation orchestration
Idempotent tasks: safe reruns and backfills
Cross-DAG dependencies with ExternalTaskSensor
Dataset-based scheduling (Airflow 2.4+)
Late data handling with catchup and backfill

Module 6: Testing & Code Quality

DAG validation tests with pytest
Task unit tests: mocking operators and hooks
Integration tests with test containers
DAG import error detection
Pre-commit hooks for Airflow best practices
CI/CD pipeline for DAG deployment

Module 7: Deployment & Operations

CeleryExecutor with Redis: distributed task execution
KubernetesExecutor: pod-per-task model
Helm chart deployment for Airflow on Kubernetes
Airflow on MWAA (Amazon Managed Airflow)
Astronomer: managed Airflow platform
Log management: remote logging to S3 or GCS

Module 8: Monitoring & Best Practices

Airflow UI: DAG view, task logs, Gantt chart
StatsD and Prometheus metrics integration
Grafana dashboards for Airflow monitoring
DAG performance: avoiding anti-patterns
Airflow best practices: idempotency, atomicity
Production Airflow architecture review

Receive Training From Our Skilled and Effective Trainers

Our Apache orchestration instructors are data engineering leads who have deployed Airflow in production for data pipeline orchestration and RabbitMQ for microservices messaging at technology companies and financial institutions. They combine workflow automation knowledge with practical messaging architecture experience.

Professional Apache Airflow Workflow Automation Certification

The Professional Apache Airflow Workflow Automation Certification validates your expertise in designing, scheduling, and managing data pipelines using Apache Airflow.

Course FAQs

1. What is Apache Airflow?

Apache Airflow is an open-source workflow orchestration platform used to schedule, automate, monitor, and manage complex data pipelines.

2. What is a DAG in Apache Airflow?

A Directed Acyclic Graph (DAG) defines the workflow, task sequence, and dependencies that Airflow executes automatically.

3. Why is Python used in Apache Airflow?

Python is used to create Airflow DAGs, automate workflows, define tasks, and integrate with various systems and services.

4. What are Airflow Operators?

Operators define individual tasks within a workflow, such as executing scripts, querying databases, sending emails, or calling APIs.

5. What are Sensors in Apache Airflow?

Sensors monitor external events or conditions and trigger workflow execution when predefined criteria are met.

6. Which industries use Apache Airflow?

Banking, healthcare, retail, telecommunications, e-commerce, finance, manufacturing, cloud computing, SaaS companies, and enterprise organizations use Apache Airflow.

7. Is Apache Airflow suitable for enterprise data engineering?

Yes. Apache Airflow is widely used to automate enterprise data pipelines, ETL workflows, machine learning pipelines, and cloud data processing.

8. Does the course include practical workflow automation projects?

Yes. The course includes hands-on projects involving ETL pipelines, API automation, cloud integration, workflow scheduling, and enterprise analytics.

9. Is Apache Airflow beginner-friendly?

Yes. Learners with basic Python and database knowledge can easily understand Airflow through structured practical training.

10. Why should professionals learn Apache Airflow?

Apache Airflow is one of the most popular workflow orchestration platforms for modern data engineering, cloud automation, and analytics pipelines.