Pentaho and Splunk Partner on Enterprise Big Data Analytics
A major channel partnership for Big Data emerged this week as data analytics and business intelligence vendor Pentaho announced a deal with Splunk (SPLK). The companies will deliver a new product, called Pentaho Business Analytics for Splunk Enterprise, to help enterprises analyze machine-generated data.
A major channel partnership for Big Data emerged this week as data analytics and business intelligence vendor Pentaho announced a deal with Splunk (SPLK). The companies will deliver a new product, called Pentaho Business Analytics for Splunk Enterprise, to help enterprises analyze machine-generated data.
For those in the audience who are not Big Data nerds, machine-generated data means the often voluminous information that computers, servers, websites, network and storage devices and systems sensors automatically spit out. Splunk was developing analytics solutions for that type of data even before the Big Data concept hit the scene with force.
Now, the partnership between Pentaho and Splunk will make it easier to integrate machine data alongside information from other sources, and analyze it from a single location to derive value for the enterprise. Users can also deploy Pentaho's enterprise-class visualization tools to help interpret the information.
Pentaho Business Analytics for Splunk Enterprise makes it possible "to combine Splunk machine data with a variety of other traditional Pentaho data sources. The bi-directional integration lets business users access, explore, analyze and visualize machine data to extract real, actionable information," according to Eddie White, executive vice president, business development at Pentaho. "Our alliance with Splunk will make it much faster and easier for users to manage and gain insight from this highly valuable source of data, combined with other data for context."
This is a very significant partnership for Pentaho, which offers a growing suite of data analytics and business intelligence tools. It will also push the channel to conceptualize Big Data in even bigger terms by helping to lower the barrier separating machine-generated data — which has traditionally been treated as something different — from the types of information typically associated with Big Data.
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