Workflow Orchestration with Apache Airflow
DAG design, scheduling, retries, sensors, testing and running reliable pipelines in production.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Sat, 19 Dec 2026 · 24 hours · 3 days
Data EngineeringWorkflow Orchestration with Apache Airflow
Duration: 24 hours · 3 days
Next batch: Sat, 19 Dec 2026
Format: Online live · Bengaluru classroom
Modules: 7 · Topics: 34
Level: Intermediate
Fee: ₹10,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| Workflow Orchestration with Apache Airflow | Sat, 19 Dec 2026 | Online live | Weekend (Sat) | 4 Saturdays | Enquire |
| Workflow Orchestration with Apache Airflow | Wed, 24 Feb 2027 | Bengaluru classroom | Weekday | 3 days | Enquire |
Soaring demand, and where it takes you
Workflow orchestration is a core part of most data platforms, and Airflow is among the most widely adopted tools.
Become an expert
Roles this programme prepares you for:
Become a Data Engineer
Builds and maintains orchestrated pipelines.
- Design clear, idempotent DAGs
- Use operators, sensors and hooks
- Handle scheduling, catchup and backfills
About the course
Orchestrate reliable data pipelines with Apache Airflow: DAG design, operators and sensors, scheduling and backfills, retries and alerting, testing and deployment practices.
DAG design, scheduling, retries, sensors, testing and running reliable pipelines in production.
Why should I attend?
Data engineers
Platform engineers
ETL developers moving from legacy schedulers
Analytics engineers
Prerequisites
Python; basic SQL; command-line familiarity
Level
Intermediate
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 24 hours · 3 days
Course fee (open batch): ₹10,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Design clear, idempotent DAGs
- Use operators, sensors and hooks
- Handle scheduling, catchup and backfills
- Configure retries, SLAs and alerts
- Test DAGs before deployment
- Deploy and monitor Airflow in production
Skills you will gain
DAG design
Reliability
Operations
Tools and platforms covered
Apache Airflow
Python
Docker
PostgreSQL
dbt
GitCourse curriculum
M01 Orchestration concepts
- Why orchestrate
- Airflow architecture
- Executors
- Scheduler, webserver and workers
- Local setup with Docker
🧪 Hands-on: Run Airflow locally
M02 Building DAGs
- Tasks and dependencies
- TaskFlow API
- Templating
- Dynamic task mapping
- Task groups
🧪 Hands-on: Build a first DAG
M03 Operators and sensors
- Common operators
- Sensors
- Hooks and connections
- Deferrable operators
- Custom operators
🧪 Hands-on: Integrate a database and an API
M04 Scheduling
- Schedules and data intervals
- Catchup and backfill
- Datasets
- Timetables
- Data-aware scheduling
🧪 Hands-on: Backfill a pipeline
M05 Reliability
- Retries
- Idempotency
- Alerts and SLAs
- Pools and priority
- Failure callbacks
🧪 Hands-on: Make a DAG fail safely
M06 Testing and deployment
- DAG tests
- CI/CD
- Monitoring
- Environment promotion
- Upgrade considerations
🧪 Hands-on: Add tests and a CI check
M07 Capstone: production DAG suite
- Design
- Build
- Test
- Deploy
🧪 Hands-on: Ship a tested set of DAGs with alerts through CI
Hands-on projects
Production-style DAG
A tested, idempotent pipeline with alerting and backfill support.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for Workflow Orchestration with Apache Airflow. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
Which Airflow version?
Labs use a current Airflow release; we note differences from older versions you may run.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹10,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
24 hours · 3 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
Orchestrate reliable data pipelines with Apache Airflow: DAG design, operators and sensors, scheduling and backfills, retries and alerting, testing and deployment practices.
What is Workflow Orchestration with Apache Airflow?
DAG design, scheduling, retries, sensors, testing and running reliable pipelines in production.
Who this course is for
Data engineers, Platform engineers, ETL developers moving from legacy schedulers, Analytics engineers.
What you will be able to do
Design clear, idempotent DAGs. Use operators, sensors and hooks. Handle scheduling, catchup and backfills. Configure retries, SLAs and alerts. Test DAGs before deployment. Deploy and monitor Airflow in production.
Career paths
Roles this course supports include Data Engineer, Data Platform Engineer, DataOps Engineer. Workflow orchestration is a core part of most data platforms, and Airflow is among the most widely adopted tools.
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