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News

  • September 30, 2015 30 Sep'15

    MongoDB 3.0 rides WiredTiger engine to boost database speeds

    The latest version of MongoDB finds the NoSQL database running on a new WiredTiger storage engine. Better performance and data compression are among MongoDB 3.0's touted benefits.

  • September 30, 2015 30 Sep'15

    DataStax Cassandra moves ahead with Azure deal and graph store

    The DataStax Cassandra engine, officially called DataStax Enterprise, is now Spark-certified. The move is one of several for the NoSQL database on a possible upswing, further evidenced by a new deal with Microsoft.

  • September 28, 2015 28 Sep'15

    Strengthening Data Preparation with Data Virtualization

    Self-service analytics allows users to design and develop their own reports and do their own data analysis with minimal support by IT. Most recently, due to the availability of tools, such as those ...

  • September 22, 2015 22 Sep'15

    Madsen looks at shift in big data analytics applications

    At a TDWI Boston Chapter meeting, Mark Madsen says some notions of information become outdated in the face of big data analytics. This is part one of two.

  • September 22, 2015 22 Sep'15

    New models of processing stalk big data analytics applications

    Operations and big data analytics applications are beginning to blend, causing changes in data strategies, Mark Madsen tells a TDWI Boston Chapter meeting. This is part two of two.

  • September 22, 2015 22 Sep'15

    Big Data Myth 2: Analytics Requires Big data

    In Part 1 of this series on big data myths I indicated that the goal of most big data projects is analytics. In other words, big data systems are almost always developed to improve the analytical ...

  • September 18, 2015 18 Sep'15

    Big Data Myth 1: Big Data is the Goal

    Big data is an incredibly popular topic. Plentiful articles, books, and blogs have been written on this topic and countless sessions have been presented that discuss some aspect of big data. I am a ...

  • September 17, 2015 17 Sep'15

    Low adoption nags Hadoop technology; data storytelling rises

    The latest episode of BizApps Today examines barriers to Hadoop technology adoption, SQL-on-Hadoop options and the new concept of data storytelling.

  • September 10, 2015 10 Sep'15

    Alteryx software targets Amazon big data

    The latest Alteryx analytics engine gets enterprises close to Amazon big data, with nods to spreadsheet aficionados and R language.

  • September 09, 2015 09 Sep'15

    Evaluating SQL-on-Hadoop tools? Start with the use case

    In a Q&A, Clarity Solution Group CTO Tripp Smith says to base SQL-on-Hadoop software decisions on actual workloads. Some Hadoop tools target batch jobs, while others are intended for interactive ones.

  • August 31, 2015 31 Aug'15

    IBM pushes MPP engine to boost cloud database services line

    IBM has added massively parallel processing and R language support to its dashDB software -- important steps for a data warehouse database tailored for distributed cloud computing.

  • August 18, 2015 18 Aug'15

    Finding a way off data isolation island

    New technology is supposed to eliminate problems. With data isolation, new business tools are only making the longstanding issue worse. Companies are amassing on-premises tools and cloud applications. And those cloud apps come in many varieties: ...

  • August 18, 2015 18 Aug'15

    Vertica 'Excavator' release adds Kafka messaging and more

    HP's Vertica analytics platform update will include better streaming data support via Kafka, and SQL-on-Hadoop improvements.

  • August 14, 2015 14 Aug'15

    DataTorrent data ingestion tool aims to speed Hadoop feeds

    A new data ingestion and extraction tool supporting the Hadoop Distributed File System is at the heart of startup vendor DataTorrent's efforts to broaden its big data analytics engine's appeal.

  • August 13, 2015 13 Aug'15

    Don't throw out design principles when jumping in Hadoop data lake

    In a Q&A, data warehousing expert Joe Caserta explains why a new generation of developers building Hadoop clusters and other big data systems may need an introduction to some fundamental rules of ETL.

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