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Big Data verstehen: Hadoop-Analytics für Unternehmen

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Hadoop Data Management Platforms and Appliances

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 systems from a variety of vendors.

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  • Focus: Open source

    Open source used to be an alternative to commercial off –the-shelf software. Today, the largest commercial software providers are big supporters of open source technologies. Enterprises are finding their software stacks increasingly rely on open source components, and developers have access to extensive open source repositories, which they can draw on to help them produce code more efficiently or to work around a particular technical problem they may have. Some enterprises are also contributing code back, supporting open source development, while at the same time, enabling a wide pool of open source developers to improve the code. Some companies are also starting to use open source to foster interest and develop a community to bolster their digitally-enabled product strategies.

  • Spark Muscles Into Big Data Processing

    It comes as no great surprise that the Apache Spark architecture has been horning in on the batch processing domain once controlled by Hadoop's MapReduce. But that's only part of the story. With data processing, streaming and machine learning capabilities on its résumé, the open source engine is learning to get along entirely without Hadoop in certain applications. In fact, one industry analyst cautiously sees a day when Spark could declare total independence, potentially bust up Hadoop cluster dominance and link separately with other Apache technologies. 

    In this three-part handbook, senior news writer Jack Vaughan examines the distinct advantages the Apache Spark architecture has over MapReduce. Also highlighted is how Spark's ability to process and analyze streaming data is helping detect fraudulent activities at a major banking and credit-card company. Next, Spark 2.0's upcoming upgrades to analytics speed, machine learning libraries, SQL support and stream processing are detailed. To close, Vaughan and senior news writer Ed Burns look at combining Spark and NoSQL databases in operational analytics applications, which could help broaden the use of both technologies.

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