Why do we need Hadoop?

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Definition Hadoop This definition is part of our Essential Guide: Using big data and Hadoop 2: New version enables new applications Sponsored News Better Together: Hadoop and Your Enterprise Data Warehouse –SAS Institute Inc. A Cost-Effective Hadoop Cluster for Big Data Environments –Dell See More Vendor Resources A Guide to Easing Your Big Data and Hadoop Management –Rackspace SQL, Hadoop Make a Powerful Data Pair –SearchDataManagement Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts (also called fragments or blocks) can be run on any node in the cluster. Doug Cutting, Hadoop's creator, named the framework after his child's stuffed toy elephant. The current Apache Hadoop ecosystem consists of the Hadoop kernel, MapReduce, the Hadoop distributed file system (HDFS) and a number of related projects such as Apache Hive, HBase and Zookeeper.

Big data

FACTS Computer Software L.L.C

Big-Data Hadoop Tutorials - MindScripts Technologies, Pune

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Hadoop distributed file system

Anshul Bhatnagar

HDFS is a distributed file system designed to run on commodity hardware. It provides high-performance access to big data across Hadoop clusters and supports big data analytics applications in a low-cost manner. The NameNode stores metadata and manages the file system namespace, while DataNodes store file data in blocks and handle replication for fault tolerance. Clients interact with the NameNode for file operations like writing blocks to DataNodes for storage and reading file blocks.

Hadoop & MapReduce

Newvewm

This is a deck of slides from a recent meetup of AWS Usergroup Greece, presented by Ioannis Konstantinou from the National Technical University of Athens. The presentation gives an overview of the Map Reduce framework and a description of its open source implementation (Hadoop). Amazon's own Elastic Map Reduce (EMR) service is also mentioned. With the growing interest on Big Data this is a good introduction to the subject.

Hadoop HDFS Detailed Introduction

Hanborq Inc.

The document provides an overview of the Hadoop Distributed File System (HDFS). It describes HDFS's master-slave architecture with a single NameNode master and multiple DataNode slaves. The NameNode manages filesystem metadata and data placement, while DataNodes store data blocks. The document outlines HDFS components like the SecondaryNameNode, DataNodes, and how files are written and read. It also discusses high availability solutions, operational tools, and the future of HDFS.

Hadoop

Nishant Gandhi

Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.

Hadoop introduction , Why and What is Hadoop ?

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