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What are the core components of Hadoop and advantages of Hadoop?

Core Components of Hadoop

Core Components of Hadoop

HDFS (Hadoop Distributed File System)
  • HDFS is the storage component of Hadoop, designed to handle vast datasets in a distributed computing environment. It splits large files into smaller blocks and stores them across nodes. It provides fault tolerance by replicating blocks on multiple nodes, ensuring data availability in case of failures.
YARN (Yet Another Resource Negotiator)
  • YARN is the resource management layer in Hadoop. It manages resources across clusters and schedules jobs based on capacity. It supports diverse applications such as MapReduce and Spark, making Hadoop versatile and efficient.
MapReduce
  • MapReduce processes large datasets in parallel across nodes using a two-phase approach: Map (transforming input into intermediate data) and Reduce (aggregating intermediate data into the final output).
Hadoop Common
  • Hadoop Common includes libraries and utilities for HDFS, YARN, and MapReduce. It provides essential tools like file system libraries, serialization libraries, and configuration utilities.
Advantages of Hadoop
  • Scalability: Supports horizontal scaling to manage petabytes of data.
  • Cost-effectiveness: Runs on commodity hardware, reducing storage and computation costs.
  • Fault tolerance: Data replication ensures high availability even during node failures.
  • Flexibility: Handles structured, semi-structured, and unstructured data types.
  • High performance: Parallel processing improves speed and efficiency.
  • Open-source: Free to use with strong community support.
  • Ecosystem integration: Integrates with tools like Hive, Spark, and Pig for diverse use cases.
  • Data locality: Processes data where it is stored, reducing network latency and cost.