Phase 2: Enterprise Data Engineering · 55 min · Python · PySpark · Kubernetes
PySpark for Distributed Data Processing
When pandas runs out of memory, Spark takes over.
Hiring signal: FDE roles at Databricks specifically test PySpark knowledge. Candidates who can translate pandas operations to Spark DataFrame API, handle data skew with salting, and choose between broadcast vs. shuffle joins pass. Candidates who attempt to use pandas for billion-row datasets fail. OOM handling and adaptive query execution knowledge demonstrate production experience.
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
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.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers PySpark DataFrame API: Pandas-to-Spark Translation, Partitioning and Data Skew, Handling OOM: Broadcast Joins and AQE, Deploying Spark on Kubernetes, Choosing Between Spark, SQL, and Pandas, Practical Application, What Hiring Managers Look For, Resources, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.
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