Harnessing the Power of Big Data with Geospatial Mapping At the Splunk User 2014 Conference today, Splunk unveiled a bevy of new offerings that are intended to make machine data more accessible to the average end user than ever. Splunk unveiled Splunk MINT Express, a cloud service that makes it easier to build analytics applications […]
Harnessing the Power of Big Data with Geospatial Mapping
At the Splunk User 2014 Conference today, Splunk unveiled a bevy of new offerings that are intended to make machine data more accessible to the average end user than ever.
Splunk unveiled Splunk MINT Express, a cloud service that makes it easier to build analytics applications that combine mobile data with data stored in either Splunk Enterprise or Splunk Cloud. In addition, Splunk released version 6.2 of Splunk Enterprise, which makes simplified analysis and pattern detection that enables more users across IT and the business to discover relationships in their data, while at the same time increasing the number of concurrent searches that can be performed and eliminating shared storage requirements.
Finally, Splunk announced today that it has partnered with Amazon Web Services (AWS) to make Hunk: Splunk Analytics for Hadoop and NoSQL Data Stores available via the Amazon Elastic MapReduce (Amazon EMR) console on an hourly basis.
Bill Emmett, director of solutions marketing for Splunk, says that, collectively, the new offerings from Splunk are all intended to make accessible machine data that is now only accessible to IT professionals; increasingly, end users need access to that information to make critical business decisions. To that end, Splunk Enterprise 6.2 now includes, for example, instant pivot and enhanced event pattern detection tools and sandbox capability that allows organizations to develop an application without having to set up a Hadoop cluster. Hunk running on AWS, meanwhile, includes a set of tools that make it much simpler to explore machine data.
Once the sole province of IT professionals that could decipher its meaning, machine data is now a critical source of information and insight for end users trying to gain insight into any number of processes. The challenge, of course, is turning massive amounts of that machine data into something that the average end user would actually consider to be actionable intelligence.
Michael Vizard is a seasoned IT journalist, with nearly 30 years of experience writing and editing about enterprise IT issues. He is a contributor to publications including Programmableweb, IT Business Edge, CIOinsight and UBM Tech. He formerly was editorial director for Ziff-Davis Enterprise, where he launched the company’s custom content division, and has also served as editor in chief for CRN and InfoWorld. He also has held editorial positions at PC Week, Computerworld and Digital Review.
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