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