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Apache Airflow on AWS (MWAA)

Orchestration, Monitoring, and Control of Real-World Data Pipelines

Course Summary

Apache Airflow is not a core requirement to become a data engineer.
But when combined with strong fundamentals, it becomes a clear career differentiator.

This course is part of the RADE™ Career Differentiators for Data Engineers track —
designed to help capable engineers stand out in interviews and system-design discussions.


 Who This Course Is For

This course is ideal for you if:

  • You already understand core data engineering concepts
    (SQL, ETL pipelines, Spark/Glue, data warehousing basics)

  • You want to move beyond “script-based pipelines”

  • You want to confidently explain:

    • How pipelines are scheduled

    • How failures are handled

    • How dependencies are managed

  • You see Airflow as a way to strengthen your resume and interview profile

  • You want real orchestration experience without running Airflow infra yourself

  • You are preparing for mid-to-senior data engineering interviews

This course is especially valuable if:

  • Your current role does not expose you to Airflow

  • You want system-level talking points for interviews

  • You want to look stronger than “Spark + SQL only” candidates

 Who This Course Is NOT For

This course is NOT recommended if:

  • You are new to data engineering

  • You are looking for a tool-only crash course

  • You expect Airflow to teach you data processing
    (Airflow is orchestration — not Spark/ETL execution)

  • You want Airflow to replace core skills
    (it won’t — and shouldn’t)

 If your fundamentals are weak, this course will not help —
it is meant to amplify, not compensate.

 What You Will Be Able to Do After This Course

By the end of this course, you will be able to:

Think Like an Orchestration Engineer

  • Explain why Airflow exists and where it fits in a data platform

  • Clearly differentiate Airflow from Step Functions and EventBridge

  • Use Airflow only for orchestration, not heavy processing

Build and Operate Real DAGs

  • Deploy and manage AWS MWAA

  • Write clean, production-grade DAGs

  • Configure retries, schedules, catchup, and dependencies correctly

  • Pause, resume, and manually trigger pipelines safely

Handle Real-World Scenarios

  • Implement failure notifications and callbacks

  • Wait for external data using sensors

  • Use dataset-based dependencies for data-aware pipelines

  • Pass metadata safely using XCom

  • Make pipelines configurable using Variables

Explain Airflow Confidently in Interviews

  • Walk through Airflow architecture and components

  • Answer scenario-based interview questions clearly

  • Explain trade-offs and design decisions

  • Demonstrate practical understanding — not buzzwords

 Hands-On & Assessment Coverage

This course includes:

  • Guided hands-on exercises on AWS MWAA

  • Practical DAG development scenarios

  • Interview question frameworks

  • MCQ-based knowledge validation

  • Assignments that simulate real-world orchestration patterns



 Live Practice Component (Important)

In addition to recorded content:

  • A live session will be conducted

  • Students will actively:

    • Explore the Airflow UI

    • Work with DAGs

    • Observe orchestration behavior

    • Apply concepts interactively

The live component focuses on doing, not theory repetition.

Core skills qualify you.

Career differentiators make you stand out.

This course does not make you a data engineer.
It makes a good data engineer look stronger.

Course Curriculum

Sachin Chandrashekhar

Lead Data Engineer @ World's #1 Airline

Course Pricing

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