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
Explore Apache Spark in the interactive universe or train with live cohorts.
Want this Apache Spark learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
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