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Part 1 — Setting the North Star - Your Data Engineering Journey & Where PySpark Fits
1 Lesson-
StartWhat do I need to know
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Part 3 — PySpark Foundations - Working with DataFrames, Files, and Core Transformations
4 Lessons
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PySpark Skill Booster for AWS Data Engineering
A practical, hands-on introduction to PySpark using AWS Glue — built for real projects, not theory.
Course Summary
PySpark is one of the most important skills for modern data engineers — but learning it the wrong way can quickly become overwhelming.
This PySpark Skill Booster is designed to give you a clear, practical foundation in PySpark the way it’s actually used in real AWS data engineering projects.
Instead of jumping straight into deep Spark internals, this course focuses on:
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Understanding where PySpark fits in real-world data pipelines
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Learning PySpark through AWS Glue notebooks, not abstract examples
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Practicing the same transformations you already know from SQL, using PySpark DataFrames
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Building confidence before going deeper into advanced Spark concepts later
You’ll work hands-on with:
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Reading and writing data from S3
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Transforming data using PySpark DataFrames
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Aggregations, joins, and NULL handling
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Parquet format and data lake best practices
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Realistic AWS Glue setups with cost awareness and cleanup
This is not a full Spark mastery course — and that’s intentional.
It’s a skill booster that prepares you to:
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Understand PySpark code in real projects
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Use PySpark confidently in AWS Glue jobs
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Perform well in PySpark interviews at a foundational level
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Know exactly what to learn next and why
If you stay consistent and complete the exercises, you’ll walk away with clarity, confidence, and a strong base to build advanced Spark skills later in the RADE journey.
Course Curriculum
