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Distributed Computing Frameworks for Big Data Processing

Traditional computing architectures cannot handle massive data volumes efficiently. Distributed computing frameworks enable parallelized execution of tasks across multiple machines.

1. Hadoop MapReduce

  • Implements a two-phase processing model (Map and Reduce) to distribute tasks across a cluster.
  • Uses YARN (Yet Another Resource Negotiator) for resource allocation and task scheduling.
  • Handles fault tolerance by replicating data blocks across nodes.

2. Apache Spark

  • Uses an RDD (Resilient Distributed Dataset) model for fault-tolerant parallel computation.
  • Supports in-memory processing, making it 100x faster than Hadoop for iterative workloads.
  • Provides built-in libraries for ML (MLlib), graph processing (GraphX), and structured data (Spark SQL).

3. Apache Flink

  • Optimized for event-driven stream processing with low-latency execution.
  • Uses stateful processing to maintain event states in real-time applications.
  • Offers exactly-once semantics, ensuring data consistency in distributed environments.