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Data engineering & AI · Corporate training

Apache Airflow

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

  • Intermediate
  • Live online and in person
  • Duration tailored to your team
Discuss this trainingExplore sample curriculum ↓

Sample curriculum

What your team can learn.

This outline is a starting point. Modules, exercises, and depth are adapted to your team's experience and project requirements.

01Workflow foundations
  • DAGs, tasks, and dependencies
  • Local environment and scheduler concepts
  • Defining a first Python workflow
02Scheduling & data flow
  • Schedules and logical dates
  • Task parameters and connections
  • Passing metadata between tasks
03Reliable pipelines
  • Retries, timeouts, and failure handling
  • Idempotent tasks and backfills
  • Data validation and monitoring
04Testing & operations
  • Testing DAG structure and task logic
  • Configuration and secrets
  • Logs, troubleshooting, and project review

Hands-on capstone

Put the learning into practice.

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

How the training works

Live demonstrations, guided exercises, pair work, and project reviews connect the concepts to practical implementation.

We agree on your learning goals, prerequisites, group size, delivery format, and schedule before shaping the final curriculum and proposal.

Your trainer

I'm Ragav Kumar V, a corporate trainer and mentor with 10+ years of training experience since 2016, 100+ batches delivered, and 3,000+ professionals trained across full-stack development, data engineering, and Cloud & DevOps.

Continue your learning path.