Data & Analytics

09:31 AM
Doug Henschen, InformationWeek
Doug Henschen, InformationWeek
Commentary
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16 Top Big Data Analytics Platforms

Data analysis is a do-or-die requirement for today's businesses. We analyze notable vendor choices, from Hadoop upstarts to traditional database players.

Revolutionary. That pretty much describes the data analysis time in which we live. Businesses grapple with huge quantities and varieties of data on one hand, and ever-faster expectations for analysis on the other. The vendor community is responding by providing highly distributed architectures and new levels of memory and processing power. Upstarts also exploit the open-source licensing model, which is not new, but is increasingly accepted and even sought out by data-management professionals.

Apache Hadoop, a nine-year-old open-source data-processing platform first used by Internet giants including Yahoo and Facebook, leads the big-data revolution. Cloudera introduced commercial support for enterprises in 2008, and MapR and Hortonworks piled on in 2009 and 2011, respectively. Among data-management incumbents, IBM and EMC-spinout Pivotal each has introduced its own Hadoop distribution. Microsoft and Teradata offer complementary software and first-line support for Hortonworks' platform. Oracle resells and supports Cloudera, while HP, SAP, and others act more like Switzerland, working with multiple Hadoop software providers.

In-memory analysis gains steam as Moore's Law brings us faster, more affordable, and more-memory-rich processors. SAP has been the biggest champion of the in-memory approach with its Hana platform, but Microsoft and Oracle are now poised to introduce in-memory options for their flagship databases. Focused analytical database vendors including Actian, HP Vertica, Kognitio, and Teradata have introduced options for high-RAM-to-disk ratios, along with tools to place specific data into memory for ultra-fast analysis.

[Read the rest on InformationWeek]

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