Apache Airflow
Develop the skills to orchestrate a retry-safe scheduled data workflow through a learning path centred on Apache Airflow. Explore concepts, guided practice and an applied project.
Build practical Apache Airflow skills.
Skills and knowledge developed in this course.
- Airflow architecture
- Workflow authoring
- Scheduling semantics
- Workflow operations
- Applied project: Orchestrate a retry-safe scheduled data workflow
- Apache Airflow — validation and review
About this course
This course develops practical capability in Apache Airflow. Its course-specific content covers Airflow architecture, Workflow authoring, Scheduling semantics, Workflow operations, followed by an applied project focused on orchestrate a retry-safe scheduled data workflow. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with Apache Airflow or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of data engineering & analytics. Prior experience with the named product helps with the practical exercises.
Orchestrate a retry-safe scheduled data workflow
Deliver orchestrate a retry-safe scheduled data workflow. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Airflow architecture
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- DAGs, tasks and scheduler responsibilities
- Executors and deployment components
Practical exerciseApply airflow architecture to the orchestrate a retry-safe scheduled data workflow and capture the validation evidence.
02Workflow authoring
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- Dependencies and task configuration
- TaskFlow patterns and reusable operators
Practical exerciseApply workflow authoring to the orchestrate a retry-safe scheduled data workflow and capture the validation evidence.
03Scheduling semantics
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- Data intervals, catchup and backfills
- Retries, timeouts and idempotent task design
Practical exerciseApply scheduling semantics to the orchestrate a retry-safe scheduled data workflow and capture the validation evidence.
04Workflow operations
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- Connections, secrets and task logs
- Testing, reruns and failed-task recovery
Practical exerciseApply workflow operations to the orchestrate a retry-safe scheduled data workflow and capture the validation evidence.
05Applied project: Orchestrate a retry-safe scheduled data workflow
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- Define inputs, assumptions and acceptance criteria for: Orchestrate a retry-safe scheduled data workflow.
- Implement the project using airflow architecture, workflow authoring, scheduling semantics, workflow operations.
Practical exerciseOrchestrate a retry-safe scheduled data workflow.
06Apache Airflow — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Airflow architecture · Workflow authoring
Establish the Apache Airflow scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Scheduling semantics · Workflow operations
Apply the core Apache Airflow techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for orchestrate a retry-safe scheduled data workflow. - 04Learning milestone 4 In progress
Deliver the applied project
Complete orchestrate a retry-safe scheduled data workflow, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| Data Engineer | ₹8–15 LPA | ₹17–28 LPA | ₹29–50 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗