Freed from the constraints of storage, network and memory, many big data analytics systems now are routinely revealing themselves to be compute bound. To compensate, big data analytic systems often result in wide horizontal sprawl (300-node Spark or NoSQL clusters are not unusual!)— to bring in enough compute for the task at hand. High system complexity and crushing operational costs often result. As the world shifts from physical to virtual assets and methods of engagement, there is an increasing need for systems of intelligence to deliver contextually relevant systems of engagement, alongside more traditional systems of record. New approaches to data processing are required to support the real-time processing of data required to drive these systems of intelligence.
Join Matt Aslett, Research Director, Data Platforms & Analytics at 451 Research, Dipti Borkar, VP Product Marketing at Kinetica to learn:
- An overview of the business and technical trends driving widespread interest in real-time analytics
- Why systems of engagement require systems of intelligence and new approaches to data processing
- How a new class of solution—a GPU-accelerated, scale out, in-memory database–can bring you orders of magnitude more compute power, significantly smaller hardware footprint, and unrivaled analytic capabilities.
- Hear how other companies in a variety of industries, such as financial services, entertainment, pharmaceutical, and oil and gas, benefit from augmenting their legacy systems with a modern analytics database.
