Apache Spark

Distributed data processing for large-scale feature engineering and training data.

Maturity level: L2 — Can Build

Six perspectives on Apache Spark

Roadmap

Learn when data exceeds single-machine pandas — common in platform roles.

Architecture

Batch data layer feeding feature stores and training.

Company

Data-heavy ML and platform engineering roles.

Projects

Spark job in a data pipeline capstone.

Interview

Distributed data fundamentals.

Career

AI platform and data engineering overlap.

What & Why

What: Unified analytics engine for large-scale data processing with Python (PySpark) and SQL.

Why: Enterprise ML starts with terabytes of data — Spark is how teams process it.

Build this

PySpark pipeline that cleans and featurizes a dataset for ML training.

Production reality

  • ! Shuffle bottlenecks
  • ! OOM on executors
  • ! Small file problems

Interview preparation

  • When Spark vs pandas?
  • Optimize a slow Spark job

Connected skills

PythonSQLFeature Stores

Explore Apache Spark in the interactive universe or train with live cohorts.

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