Hadoop Framework

Hadoop is an open-source, distributed computing framework designed to store and process large volumes of data across a cluster of commodity hardware. It was initially developed by Apache Software Foundation and is now a part of the Apache Hadoop project. Hadoop provides a reliable, scalable, and fault-tolerant platform for handling big data. The core components of the Hadoop framework include:

1. Hadoop Distributed File System (HDFS):

   • HDFS is the primary storage system in Hadoop. It is a distributed file system that stores data across multiple nodes in a cluster. HDFS is designed for high-throughput data access and fault tolerance. Data is divided into blocks and distributed across the cluster for redundancy and reliability.

2. MapReduce:

   • MapReduce is a programming model and processing framework used for distributed data processing in Hadoop. It allows users to write applications that process large datasets in parallel by dividing tasks into two phases: the “Map” phase for data processing and the “Reduce” phase for aggregation. MapReduce jobs can be written in various programming languages, including Java, Python, and more.

3. YARN (Yet Another Resource Negotiator):

   • YARN is the resource management and job scheduling component of Hadoop. It manages cluster resources and allocates them to various applications, including MapReduce jobs and other data processing frameworks like Apache Spark. YARN allows multiple applications to run on the same Hadoop cluster, improving resource utilization.

4. Hadoop Common:

   • Hadoop Common includes libraries and utilities that provide the foundation for the Hadoop ecosystem. It includes common tools, configurations, and dependencies that are used by various Hadoop components.

5. Hadoop Ecosystem:

   • Hadoop has a rich ecosystem of additional components and projects that extend its capabilities for various data processing tasks. Some popular Hadoop ecosystem projects include:
     • Apache Hive: A data warehouse infrastructure that provides SQL-like query language (HiveQL) for querying and analyzing data stored in HDFS.
     • Apache Pig: A high-level scripting language and platform for data transformation and analysis.
     • Apache HBase: A NoSQL database that runs on top of HDFS, designed for real-time, random read and write access.
     • Apache Spark: An in-memory, distributed data processing framework that provides faster and more flexible data processing compared to MapReduce.
     • Apache Kafka: A distributed event streaming platform for real-time data ingestion and processing.

6. Hadoop Clients:

   • Hadoop provides client libraries and command-line tools that allow users to interact with Hadoop clusters, submit jobs, and manage data.