Big Data is only as valuable as the analysis that is derived from it. In an effort to accelerate the time to value organizations can achieve by investing in Hadoop, Splunk today announced the general availability of Hunk: Splunk Analytics for Hadoop.
By applying the Splunk virtual index technology and Splunk Search Processing Language (SPL) directly to Hadoop, Hunk is designed to allow business users to iteratively explore Big Data and more easily share analytics with each other, says Sanjay Mehta, vice president of product marketing for Splunk.
This latter capability, says Mehta, is provided by the late binding attributes of Splunk, which allow users to incorporate multiple sources of data within an analytics applications without first having to work with a database administrator to set up a database schema that can support only a limited number of pre-determined queries. By being able to iteratively explore patterns and identify anomalies, Mehta says that Hunk provides a Big Data analytics environment that when combined with the late binding attributes of Splunk, allows for enhanced levels of sharing and collaboration across the enterprise using RESTful APIs.
To promote the adoption of Hunk, Splunk has included a software development kit that makes it easier to invoke the new platform.
While Hadoop is clearly the most popular source of Big Data, Mehta says Splunk intends to develop similar offerings for other sources of Big Data, including NoSQL databases such as MongoDB and Cassandra.
While there’s no doubt that Big Data is fostering a wave of analytics innovation being driven by business users, Splunk has the added benefit of being a technology that has already been widely embraced by IT organizations to search through reams of machine data. Now Splunk is extending the scope of its reach to business users looking for an analytics framework that allows them to explore Big Data without the need for a data scientist to guide them through it.