Apache Spark

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general execution graphs.

Overview

Spark enables fast and general-purpose cluster computing with:

• In-memory computing for faster processing
• Unified platform for batch, streaming, ML, and graph processing
• Fault-tolerant distributed computing
• Easy-to-use APIs in multiple languages

Core Concepts

Resilient Distributed Datasets (RDDs)

• Fundamental data structure in Spark
• Immutable distributed collections
• Fault-tolerant through lineage tracking
• Lazy evaluation for optimization

DataFrames and Datasets

• Higher-level abstractions over RDDs
• Schema-aware data structures
• Catalyst optimizer for query optimization
• Type-safe operations (Datasets)

Spark Context and Session

• Entry point to Spark functionality
• Manages cluster connection and resources
• SparkSession for unified entry point

Content continues with comprehensive coverage of Spark architecture, components (Core, SQL, Streaming, MLlib, GraphX), performance characteristics, use cases, and interview topics...

Note: This is a placeholder file. The full content would include detailed sections on Spark architecture, RDD operations, DataFrame API, Spark SQL, Spark Streaming, MLlib, deployment modes, performance tuning, and common interview questions.