Description 

Apache Spark has come a long ways since it began as a faster batch oriented map-reduce solution for Hadoop.  With support for streaming, machine learning and most recently SQL, Spark aspires to become a player in the realm of real time analytics at scale. 

However, much of its underpinnings remain batch oriented and unsuited for highly concurrent OLAP workloads. 

In this talk, we will describe how we are innovating and renovating Spark's core underpinnings to make it suitable for real time operational analytics, helping create a unified platform that supports row and column data, high concurrency and approximate query processing at scale.

Agenda

6:00 – 6:15 Welcome and Networking

6:15 – 7:15 Presentation by Jags Ramnarayan

7:15 – 8:00 Food, Drinks & Discussion

Presenter

Jags Ramnarayan is the chief product visionary for SnappyData. 

Jags is the Chief Architect for “fast data” products(GemFire) at Pivotal and has served in the extended leadership team of the company. At Pivotal and previously at VMWare he led the technology direction for its high performance distributed data Grid and in-memory DB products. 

He has spent more than two decades in this industry and has a bachelors degree in computer science and a masters degree in management of science and technology.

Sponsors

Snappy Data is the presenter of the event and sponsor for food and drinks.

Pivotal is the sponsor for the hosting fees.

CentrlOffice is the host for this event.