Apache Spark
A distributed data processing engine for large-scale data transformation and machine learning — the backbone of many enterprise data platforms.
Apache Spark is the standard framework for processing data at a scale that exceeds what a single machine can handle — petabytes of logs, events, or sensor data. It's required at companies with significant data engineering infrastructure, including most large tech companies, financial institutions, and any organization running large-scale machine learning pipelines.
Typical time to job-readiness: ~2 months.
Learning Apache Spark
Beginner
Understand the RDD and DataFrame models, run basic PySpark transformations and actions, and set up a local environment or Databricks Community Edition.
Intermediate
Write Spark SQL queries, optimize jobs with partitioning, caching, and broadcast joins. Work with structured streaming for near-real-time pipelines.
Advanced
Tune executor memory and shuffle configurations, diagnose skew and spill issues, and architect production-grade pipelines on Databricks or EMR. Assessed through coding challenges on large dataset operations.
Key concepts
- Distributed computing — data split across many nodes, processed in parallel
- DataFrame API — primary abstraction; lazy evaluation until an action is called
- Transformations (lazy) vs actions (trigger execution): map/filter vs count/collect
- Partitions — how data is split across the cluster; too few or too many both hurt performance
- Shuffle — expensive data redistribution across nodes, triggered by groupBy/join/repartition
- PySpark — Python API for Spark, the most common interface in data engineering roles
Common interview topics
- Explain the difference between transformations and actions in Spark
- What is a shuffle and why is it expensive
- How do you handle data skew in a Spark job
- What is the difference between repartition and coalesce
- When would you use Spark Streaming vs a batch job