Phase 2: Enterprise Data Engineering · 55 min · Python · PySpark · Kubernetes
PySpark for Distributed Data Processing
When pandas runs out of memory, Spark takes over.
What you will learn
- Use PySpark DataFrame API: selecting, filtering, joining, aggregating — the pandas-to-Spark translation
- Control partitioning and repartitioning: avoiding skew, data salting for skewed joins
- Handle OOM: broadcast joins, adaptive query execution, memory tuning
- Deploy Spark on Kubernetes: resource allocation, executor configuration
- Choose between Spark, SQL, and pandas based on data volume and latency requirements
Objective
What you'll learn
This lesson takes approximately 55 min. By the end, you will be able to:
- Use PySpark DataFrame API: selecting, filtering, joining, aggregating — the pandas-to-Spark translation
- Control partitioning and repartitioning: avoiding skew, data salting for skewed joins
- Handle OOM: broadcast joins, adaptive query execution, memory tuning
- Deploy Spark on Kubernetes: resource allocation, executor configuration
- Choose between Spark, SQL, and pandas based on data volume and latency requirements
The Problem
When your client's data doesn't fit in pandas — when you're processing 50GB of documents for RAG ingestion or joining billion-row tables — you need distributed processing. PySpark is the tool FDEs reach for when single-machine processing runs out of memory.
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