05. Apache Spark Fundamentals

Apache Spark is a unified analytics engine for large-scale data processing, offering significant performance improvements over traditional MapReduce through in-memory computing and advanced optimization techniques.

Learning Objectives

• Understand Spark architecture and core concepts
• Learn RDD (Resilient Distributed Dataset) programming
• Explore Spark’s execution model and optimization
• Compare Spark with MapReduce
• Set up and configure Spark clusters
• Write Spark applications in Scala, Python, and Java

Key Topics

1. Spark Core Architecture
   • Driver and executor processes
   • Cluster managers (Standalone, YARN, Mesos, Kubernetes)
   • Spark Context and Spark Session
   • Memory management and caching
2. RDD Programming Model
   • Creating RDDs from data sources
   • Transformations vs Actions
   • Lazy evaluation and lineage
   • Partitioning and data locality
3. Performance and Optimization
   • In-memory computing advantages
   • Catalyst optimizer
   • Tungsten execution engine
   • Broadcast variables and accumulators

• 05-01 Apache Spark Fundamentals Required