Apache Spark & PySpark
Master Apache Spark fundamentals, PySpark programming, and distributed data processing for large-scale machine learning. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples
45 min•By Priygop Team•Last updated: Feb 2026
Spark Architecture
- Spark Core: Distributed computing engine
- Spark SQL: Structured data processing
- Spark Streaming: Real-time data processing
- MLlib: Machine learning library
- GraphX: Graph processing
PySpark Programming
- RDDs (Resilient Distributed Datasets): Core data structure
- DataFrames: Structured data manipulation
- Spark SQL: SQL-like queries on DataFrames
- UDFs (User Defined Functions): Custom functions
Spark Operations
- Transformations: Lazy operations (map, filter, groupBy)
- Actions: Eager operations (collect, count, save)
- Caching: Persist data in memory
- Partitioning: Optimize data distribution
Performance Optimization
- Memory Management: Tune memory allocation
- Partitioning strategy: Optimize data partitioning
- Broadcast Variables: Share data across nodes
- Accumulators: Global counters and aggregators