News

News

  • May 19, 2016 19 May'16

    Oracle NoSQL: An oxymoron waiting to happen?

    New cloud apps seem ready-made for NoSQL. This may cause Oracle to put more focus on its Oracle NoSQL database, which is often overlooked amid a crush of NoSQL contenders.

  • May 16, 2016 16 May'16

    New data landscape augurs discovery-based architectures

    In an interview, consultant Lakshmi Randall foresees changes in how data management is organized and executed as the overall data landscape shifts due to the adoption of big data systems.

  • April 29, 2016 29 Apr'16

    Big data challenges traditional data modeling techniques

    Surging big data is changing data modeling techniques, including schema creation. The word from Enterprise Data World 2016: Data pros must adjust.

  • April 29, 2016 29 Apr'16

    EBay helps drive new style of data engineering

    Open source data engineering has become a way of life at e-commerce leader eBay, says the company's Debashis Saha. Kylin is one of the tools that has resulted.

  • April 22, 2016 22 Apr'16

    Data lake meets warehouse in hybrid data architectures

    A new view on hybrid data architectures, in which data lakes and warehouses coexist, emerged at EDW 2016. The hybrid approach has implications for data design, skills and planning.

  • Sponsored News

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  • April 19, 2016 19 Apr'16

    New tools offer a better view into managing Hadoop clusters

    Running a Hadoop cluster in the data center isn't for the weak. But several new tools aim to give IT operations teams a closer look into what's going on inside Hadoop-based big data systems.

  • April 13, 2016 13 Apr'16

    Hadoop market consolidation continues with Pivotal's exit

    Pivotal Software dropped out of the Hadoop distribution business in favor of reselling the Hortonworks version of the big data framework -- and the market consolidation moves may not be over.

  • April 01, 2016 01 Apr'16

    Streaming analytics puts data in motion at Strata + Hadoop 2016

    Moving streams of data is a must in many modern applications. As a result, streaming analytics systems with Spark Streaming, Kafka and other components are coming to the big data forefront.

  • March 31, 2016 31 Mar'16

    Deep machine learning drives Loop AI quest

    Loop AI Labs' Bart Peintner discusses the transformational impact of deep learning technology, artificial intelligence, and how these tech trends will reshape industries.

  • March 31, 2016 31 Mar'16

    Hadoop core components may not be central to big data future

    At Strata + Hadoop World 2016, Hadoop co-creator Doug Cutting said the core of the distributed processing framework is likely to see its position at the center of big data systems diminish.

  • March 25, 2016 25 Mar'16

    Unstructured data is a misnomer

    Nowadays, the term unstructured data pops up everywhere. It owes its popularity for a large part to the success of big data, to successful technologies such as NoSQL and Hadoop, and to formats such ...

  • March 24, 2016 24 Mar'16

    Strata + Hadoop World 2016: Hadoop and Spark in spotlight

    The Strata + Hadoop World conference focuses on big data management and analytics technologies, in particular the Hadoop distributed processing framework and Spark processing engine.

  • March 16, 2016 16 Mar'16

    Social media startup uses NoSQL Redis Cloud to 'scale to infinity'

    Because of growing data demands, and the need to nimbly scale up and down, a startup social networking platform chose a Redis Labs NoSQL database management system running on AWS.

  • March 02, 2016 02 Mar'16

    Hortonworks Hadoop distribution goes to two release tracks

    Looking to better balance system stability and innovation, Hadoop distribution provider Hortonworks will follow two release 'cadences' for different component sets in its HDP package.

  • February 29, 2016 29 Feb'16

    Apache Spark architecture speeds data jobs, ousts MapReduce

    Its collection of big-data processing features is priming the Apache Spark architecture for wider deployment. One key trait: Spark performance outpaces MapReduce in many Hadoop use cases.

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