Apache Hadoop

Apache Hadoop: An open-source framework for distributed storage and processing of large datasets, designed for big data analytics and applications.

Advantages

  • Scalability: Easily scales to handle vast amounts of data.
  • Fault Tolerance: Resilient to hardware failures.
  • Cost-Effective: Utilizes commodity hardware for cost savings.
  • Parallel Processing: Distributes data processing across nodes.
  • Ecosystem: Offers a wide range of tools for various big data tasks.

Disadvantages

  • Complexity: Setup and configuration can be complex.
  • Programming Model: Requires knowledge of MapReduce and related paradigms.
  • Latency: Real-time processing is a challenge.
  • Storage Overhead: Replication for fault tolerance consumes storage.
  • Resource Intensive: Demands significant computational resources.

Components

  • Hadoop Distributed File System (HDFS): Distributed file storage.
  • MapReduce: Programming model for processing data.
  • YARN (Yet Another Resource Negotiator): Resource management.
  • Hadoop Common: Shared utilities and libraries.
  • Hadoop Ecosystem: Additional projects like Hive, Pig, and Spark.

Development tools

  • Hadoop Command-Line Tools: Utilities for managing Hadoop clusters.
  • Hadoop Streaming: For writing MapReduce programs in any language.
  • Hive: A data warehousing and SQL-like query language.
  • Pig: A high-level scripting platform for data analysis.
  • Apache Spark: A fast and general-purpose data processing engine for big data.