Hadoop Technology

Hadoop is not a single technology but a comprehensive ecosystem of open-source software tools and frameworks designed for distributed storage and processing of large datasets. It was originally developed by the Apache Software Foundation and has since become a cornerstone of big data processing and analytics. Here are some key technologies and components within the Hadoop ecosystem:

1. Hadoop Distributed File System (HDFS):

   • HDFS is a distributed file system designed to store and manage large files and datasets across a cluster of commodity hardware. It divides data into blocks and replicates them across nodes in the cluster for fault tolerance.

2. MapReduce:

   • MapReduce is a programming model and processing framework for distributed data processing. It allows users to write code in the form of Map and Reduce functions to process and analyze data stored in HDFS.

3. Yet Another Resource Negotiator (YARN):

   • YARN is a resource management and job scheduling framework in Hadoop. It allows multiple data processing engines, such as MapReduce, Apache Spark, and Apache Flink, to run on the same cluster and share resources efficiently.

4. Apache Spark:

   • Apache Spark is an in-memory data processing framework that is often used in conjunction with Hadoop. It provides faster and more flexible data processing capabilities compared to traditional MapReduce.

5. Hive:

   • Hive is a data warehousing and SQL-like query language for Hadoop. It allows users to write SQL queries to analyze data stored in HDFS, making it accessible to those familiar with SQL.