Guest Blog by Jack Norris
Jack is responsible for worldwide marketing for MapR Technologies, the leading provider of a enterprise grade Hadoop platform. He has over 20 years of enterprise software marketing experience and has demonstrated success from defining new markets for small companies to increasing sales of new products for large public companies. Jack’s broad experience includes launching and establishing analytic, virtualization, and storage companies and leading marketing and business development for an early-stage cloud storage software provider.
Big Data use cases are changing the competitive dynamics for organizations with a range of operational use cases. Operational intelligence refers to applications that combine real-time, dynamic, analytics that deliver insights to business operations. Operational intelligence requires high performance. “Performance” is a word that is used quite liberally and means different things to different people. Everyone wants something faster. When was the last time you said, “No, give me the slow one”?
When it comes to operations, performance is about the ability to take advantage of market opportunities as they arise. To do this requires the ability to quickly monitor what is happening. It requires both real-time data feeds and the ability to quickly react. The beauty of Apache Hadoop, and specifically MapR’s platform, is that data can be ingested as a real-time stream; analysis can be performed directly on the data, and automated responses can be executed. This is true for a range of applications across organizations, from advertising platforms, to on-line retail recommendation engines, to fraud and security detection.
When looking at harnessing Big Data, organizations need to realize that multiple applications will need to be supported. Regardless of which application you introduce first, more will quickly follow. Not all Hadoop distributions are created equal. Or more precisely, most Hadoop distributions are very similar with only minor value-added services separating them. The exception is MapR. With the best of the Hadoop community updates coupled with MapR’s innovations, the broadest set of applications can be supported including mission-critical applications that require a depth and breadth of enterprise-grade Hadoop features.