Data Engineering · Intermediate

Apache Spark

Process large datasets with Spark SQL, DataFrames, partitioning, and performance-aware transformations.

  • 30 hoursself-paced
  • 3 units9 lessons
  • 3 labs0 projects
  • 5 hrs/weeksuggested pace
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Course overview

Move from single-machine processing to distributed computation. Understand Spark execution, design efficient transformations, and ship a maintainable analytics workload.

Who this is for

You know the fundamentals and want structured practice that turns knowledge into production habits.

Prerequisites

No previous OSEKOO ACADEMY course is required. Use the optional diagnostic to confirm your starting point.

Syllabus

Each unit combines explanation, guided practice, a checkpoint, and practical evidence. Open a unit to review the exact sequence.

1. Distributed Processing3 lessons

Lesson sequence

  1. Spark architecture and execution model · Free preview
  2. DataFrames, schemas, and types
  3. Transformations and actions
2. Spark SQL and Storage3 lessons

Lesson sequence

  1. Spark SQL workflows
  2. Joins and window functions
  3. Parquet and partitioning
3. Performance and Delivery3 lessons

Lesson sequence

  1. Query plans and optimization
  2. Testing Spark applications
  3. Capstone: distributed ETL
How mastery is verified

Assessment model

Progress is more than watching lessons. You practise, receive checkpoint feedback, and submit evidence that can be reviewed.

  1. Check your starting pointTake the optional, unscored diagnostic.
  2. Practise and check understandingComplete labs and unit checkpoints as you learn.
  3. Prove the skillComplete every lesson and required lab, score 80% on the technical exam, and earn 70% on the reviewed project.
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