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Sample curriculum

Apache Airflow

Express data workflows as code with clear task dependencies, scheduling, and failure recovery.

  • Intermediate
  • Live online and in person
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At a glance

Who it’s for

Engineers with the listed foundations who want to deepen this skill or apply it in a different stack.

Starting point: Intermediate

Bring these foundations

  • Python and familiarity with batch data pipelines.
Foundation sessions can be included when needed.

Technologies & tools

  • Apache Airflow
  • Python
  • SQL
  • Docker
Setup and versions agreed for your environment.

A practical destination

Skills to practise.

The sample path works towards these capabilities. Final goals reflect your team’s starting point and agreed scope.

  1. 01

    Model a workflow as tasks with explicit dependencies.

  2. 02

    Reason about scheduling, data flow and reruns.

  3. 03

    Test and monitor pipelines with deliberate recovery behaviour.

Sample curriculum

Follow the learning route.

Each module connects the concepts to practical work. Open a module to explore both.

01Module 1

Workflow foundations

Concepts to understand

  • DAGs, tasks, and dependencies
  • Local environment and scheduler concepts
  • Defining a first Python workflow

Practical work

Create a small DAG with independent and dependent tasks. Explain why its structure matches the data workflow.

Show the result. Explain one decision. Identify what you would improve.
02Module 2

Scheduling & data flow

Concepts to understand

  • Schedules and logical dates
  • Task parameters and connections
  • Passing metadata between tasks

Practical work

Set up scheduling and pass a small piece of task metadata. Compare a scheduled run with a manual rerun.

Show the result. Explain one decision. Identify what you would improve.
03Module 3

Reliable pipelines

Concepts to understand

  • Retries, timeouts, and failure handling
  • Idempotent tasks and backfills
  • Data validation and monitoring

Practical work

Introduce a controlled task failure and review retries and idempotency. Show how duplicate or partially completed work is avoided.

Show the result. Explain one decision. Identify what you would improve.
04Module 4

Testing & operations

Concepts to understand

  • Testing DAG structure and task logic
  • Configuration and secrets
  • Logs, troubleshooting, and project review

Practical work

Test the DAG and task logic, inspect logs and alerts, and document how an operator diagnoses and resumes a failed run.

Show the result. Explain one decision. Identify what you would improve.

A sample project

Bring the skills together.

Orchestrate a sample ETL workflow with retries, data checks, and a documented recovery procedure.

Shorter engagements can focus on selected exercises. A larger, learner-owned capstone can be scoped for a longer programme.

Review the work & reasoning

Four useful questions.

  1. 01

    Does the work meet the agreed requirements?

  2. 02

    Can the learner explain the key decisions and trade-offs?

  3. 03

    What happens with invalid input, failure or an unexpected condition?

  4. 04

    What would they change or explore next?

Designed & delivered by Ragav Kumar V.

Training since 2016 · 100+ batches · 3,000+ professionals trained

Live demonstrations, guided exercises, pair work and project reviews connect the concepts to implementation. We agree the goals, starting knowledge, cohort size, delivery and schedule before shaping the final curriculum.

Shape this for your team

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