Widespread digital transformation and the explosion of the Internet of Things has led to a dramatic increase in the volume, complexity and unpredictability of data. This has created a need for converged solutions that require a new combination of processing capabilities that are not met by traditional OLTP and OLAP implementation approaches. The challenge set by modern enterprise is to create a scalable converged platform that can simultaneously manage millions of mutations, power high throughput key lookup, do high resolution location filters against high cardinality datasets, power aggregates with millions to billions of groupings, and drive fast visualization of large location related datasets.

In this talk we will present Kinetica, a solution that solves this problem. We discuss why being able to leverage advanced manycore devices like the GPU is critical in solving the problem. Followed by a review of the choices we have made from an architectural, data structure, and kernel processing context that provide the fundamental basis of our converged solution.

Part of Hardware Accelerated Database Lectures 2018 Seminar Series

Bio:
Nima is the Chief Technology Officer, original developer and software architect of the Kinetica platform. Leveraging his unique insight into data processing, he established the core vision and goal of the Kinetica platform. Nima leads Kinetica’s technical strategy and roadmap development while also managing the engineering team. He has developed innovative big data systems across a wide spectrum of market sectors, ranging from biotechnology to high-speed trading systems using GPUs, as Lead Architect and Engineer with The Real Deal, Digital Sports, Equipoise Imaging, and Synergetic Data Systems. Early in his career, Nima was a Senior Consultant with Booz Allen Hamilton. Nima holds a B.S. in Computer Science from the University of Maryland.