Data stored but not analyzed is expensive. Even with declining storage costs it’s expensive to store and maintain. But the biggest expense is the cost of opportunities lost.
Apache Hadoop is maturing as a loosely coupled stack for inexpensive batch storage. Hadoop can store an abundance of data and has the potential to serve a variety of analytic and data science applications.
Yet trying to explore, analyze and visualize data in Hadoop has often meant significant work manually writing jobs or setting up fixed schemas, which takes time and keeps vital data out of the reach of business and IT. New tools and analytic platforms, such as Hunk: Splunk Analytics for Hadoop, allow even nontechnical users to explore and understand massive data sets. Organizations can now explore, analyze and visualize unstructured data in Hadoop in hours instead of weeks or months.
Take Socialize, a mobile applications platform company. Its real-time bidding exchanges require a response time of under 100 milliseconds. Socialize must offer marketers an advertising campaign for a mobile app and accept the highest bid — all within a tenth of a second. It uses Splunk software to make informed bidding decisions and validate campaign effectiveness.
Socialize now gains powerful business development data with Splunk, without manually writing complex MapReduce routines. “Analyses that once required weeks of laborious coding are now done in hours.”
Driving faster value from big data enables new insights and smarter decisions to stay ahead of the competition and power Operational Intelligence. Learn more at www.splunk.com/bigdata.