Big Data #1

Hadoop review

Hadoop is an open-source framework for distributed storage and processing of large datasets. It was originally created by Doug Cutting and Mike Cafarella and is now maintained by the Apache Software Foundation. Hadoop is designed to handle massive amounts of data across a cluster of commodity hardware, making it a key technology for big data processing and analytics.

The core components of Hadoop include:

Hadoop Distributed File System (HDFS): HDFS is a distributed file system designed to store large files across multiple machines in a fault-tolerant manner. It breaks data into blocks and replicates them across the cluster to ensure data durability and availability.

MapReduce: MapReduce is a programming model and processing engine for distributed data processing in Hadoop. It allows users to write programs that can process and analyze large datasets by dividing tasks into smaller sub-tasks and distributing them across the cluster.

YARN (Yet Another Resource Negotiator): YARN is a resource management and job scheduling component in Hadoop. It helps manage and allocate cluster resources to different applications, including MapReduce, Spark, and others, making the cluster more versatile and efficient.

Hadoop Common: This component includes libraries and utilities that are common to all Hadoop modules. It provides essential tools and interfaces for Hadoop's functioning.

Hadoop is often used in conjunction with other technologies and frameworks, such as Apache Hive (for SQL-like queries), Apache Pig (for data transformation), and Apache Spark (for in-memory data processing), to perform various data processing and analytics tasks on large datasets.

Hadoop has had a significant impact on the world of big data and has been instrumental in enabling organizations to store, process, and analyze vast amounts of data for insights and decision-making. However, it's worth noting that the big data ecosystem has evolved, and other technologies and frameworks have emerged alongside or even superseded Hadoop in some use cases.

In the next article I will write about architecture of HDFS.