Distributed Data Processing Guide
PySpark for
Pandas Developers
A direct, no-fluff guide to batch processing. Assumes fluency in Pandas, comfort with DataFrames, and familiarity with SQL thinking. Maps every concept to what you already know.
Execution
Lazy
Nothing runs until .collect()
Data Scale
TB+
Beyond single-machine RAM
Parallelism
Auto
Distributed across cluster
Fault Tolerance
Built-in
Lineage-based recovery
01 – 03
Foundations
Why Spark, mental model shift, environment setup
04 – 06
DataFrames
Spark vs Pandas DFs, column expressions, filtering
07 – 09
Operations
GroupBy, null handling, joins
10 – 11
Advanced
Window functions, UDFs and alternatives
12 – 14
Storage
I/O formats, partitioning, caching
15 – 16
Performance
Debugging, tuning, full mapping table