Hadoop framework

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  • Taking stock of Hadoop 2

    The release of Hadoop 2 seems to be all that prospective users and industry analysts can talk about. If you think all that chatter has translated into widespread adoption, you're wrong. The release -- a possible answer to calls for a "hardened" Hadoo... 

  • Choosing the right Hadoop platform

    Focusing on the current state of the Hadoop data management platform market, this Technology Guide provides an introduction to Hadoop and suggests criteria for selecting a Hadoop data management platform. Also included are reviews of leading Hadoop s... 

  • MapR

    MapR is a software company based out of San Jose, California that was founded in 2009. 

  • Hadoop 2

    Apache Hadoop 2 is the second iteration of the Hadoop framework for distributed data processing.  Hadoop 2 adds support for running non-batch applications as well as new features to improve system availability. 

  • Apache Pig

    Apache Pig is an open-source technology that offers a high-level mechanism for parallel programming of MapReduce jobs to be executed on Hadoop clusters. 

  • Apache Hadoop YARN (Yet Another Resource Negotiator)

    Apache Hadoop YARN (short, in self-deprecating fashion, for Yet Another Resource Negotiator) is a cluster management technology. It is one of the key features in second-generation Hadoop. 

  • JAQL (json query language)

    JAQL is a query language for the JavaScript Object Notation (JSON) data interchange format. Pronounced "jackal," JAQL is a functional, declarative programming language designed especially for working with large volumes of structured, semi-structured ... 

  • Hadoop Distributed File System (HDFS)

    HDFS is a distributed file system that provides high-performance access to data across Hadoop clusters. Like other Hadoop-related technologies, HDFS has become a key tool for managing pools of big data and supporting big data analytics applications. 

  • Hadoop cluster

    A Hadoop cluster is a special type of computational cluster designed specifically for storing and analyzing huge amounts of unstructured data in a distributed computing environment.  

  • Apache HBase

    Apache HBase is a column-oriented key/value data store built to run on top of the Hadoop Distributed File System (HDFS). 

  • in-memory data grid

    An in-memory data grid (IMDG) is a data structure that resides entirely in RAM (random access memory), and is distributed among multiple servers. 

  • Apache Hive

    Apache Hive is an open-source data warehouse system for querying and analyzing large datasets stored in Hadoop files. Hadoop is a framework for handling large datasets in a distributed computing environment. 

About Hadoop framework

This section offers a variety of information resources on deploying and managing a Hadoop cluster to support the distributed processing of large amounts of information in big data environments. Readers will find news, analysis and expert advice on Apache Hadoop and related technologies such as MapReduce, Cassandra, HBase, Hive, Pig and the Hadoop Distributed File System (HDFS). Learn about the Hadoop framework and get tips on evaluating Hadoop software, building a Hadoop architecture and managing Hadoop applications. You’ll also find Hadoop tutorials and guidance on creating a Hadoop data warehouse. Use our content to ensure that you’re up to date on the latest Hadoop developments and on how to overcome the challenges of installing a Hadoop distribution and using a Hadoop system.