Apache Airflow – Workflow Orchestration & Scheduling Course in Chennai
825 Ratings
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.
₹38000
₹45000
Case Studies and Projects
2+
Hours of Training
20+
Placement Assurance
100%
Expert Support
yes
Support & Access
Lifetime
Certification
Yes
Skill Level
All
Language
All
Course Objectives
Popular Techniques Covered in This Course
Get Hands-on Knowledge about Real-Time Projects
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.
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