Hadoop is an open-source framework designed for storing, managing, and processing vast amounts of data. It provides the essential infrastructure tools for big data analysis, enabling organizations to derive valuable insights and make informed decisions.

Key Components of Hadoop:

• HDFS (Hadoop Distributed File System): A distributed file system that runson commodity hardware, HDFS is the core of Hadoop’s storage. It divides large files into fixed-size blocks (typically 128MB or 256MB) and replicates them across different nodes in the cluster to achieve fault tolerance and high availability.
• MapReduce: A parallel data processing program that divides tasks to sub-tasks ( maps), processes them in parallel and aggregates the results ( Reduce ) . This enables large-scale data computation across clusters.
• YARN (Yet Another Resource Negotiator): Manages and schedules resources across the Hadoop cluster. coordinating program execution and psupporting multiple processing engines such as Spark, in addition to MapReduce.
• Common Utilities: A set of shared libraries and utilities required by all Hadoop components.
• Hadoop Ecosystem Tools:
  • Hive: A SQL-like query engine for data analysis.
  • Pig: A scripting language for working with large data sets.
  • HBase: A NoSQL columnar database built on Hadoop's HDFS.
  • Spark: A fast, in-memory data processing engine, often considered and improvement over MapReduce.
  • Oozie: A workflow scheduler for managing job execution order and dependencies.
  • Ambari: Provides a web-based interface and REST API for provisioning, managing, and monitoring Hadoop clusters.

Hadoop revolutionized large-scale data processing by reducing costs through the use of commodity hardware while still maintaining data consistency, reliability, and high availability. It is well-suited for batch processing and forms the foundation for many big data workflows, including log analysis, ETL pipelines, data warehousing, and fraud detection.

Key Benefits:

• Fault tolerance.
• Cost-effective storage on commodity hardware.
• Supports structured, semi-structured, and unstructured data.
• A rich ecosystem of tools.
• Flexibility in processing different data workloads.

Hadoop was pivotal in making big data processing accessible to organizations of all sizes, enabling them to leverage big data effectively..

• Apache Hadoop
• Hadoop on Ampere Arm Processors