Jags Ramnarayan | Spark Summit 2017
Channel: SiliconANGLE theCUBE
Date: 06/06/2017

Jags Ramnarayan, SnappyData, at Spark Summit 2017

#SparkSummit #theCUBE

https://siliconangle.com/2017/07/25/snappydata-enriching-spark-hybrid-database-sparksummit/

How SnappyData is enriching Spark as a hybrid database

Enriching Apache Spark so it’s not just a platform but also a store is just part of the innovation occurring at SnappyData Inc., according to Jags Ramnarayan (pictured), founder and chief technical officer of SnappyData.

Built on Spark’s open-source in-memory data processing engine, SnappyData is a high-performance in-memory data platform for mixed workload applications. The platform complements Apache Cassandra, an open-source distributed NoSQL database management system, allowing real-time work with Spark, Ramnarayan explained.

“… A single data store that’s capable of taking Spark streams, doing transactions, providing mutable state management in Spark, but most importantly being able to turn around and run analytical queries on that state that is continuously merging,” is how Ramnarayan described SnappyData to George Gilbert (@ggilbert41) and David Goad (@davidgoad), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during this year’s Spark Summit event in San Francisco, California.

Solving the open source paradigm encapsulating Spark, Mesos, Akka, Cassandra and Kafka Stack for the ability to get insight and responses on live data, SnappyData complements Cassandra, enabling real-time working in Spark giving “Google-like speeds to live data,” Ramnarayan said. “If stuff changes in Cassandra, I can have an immediate, real-time reflection of that mutable state in Spark on which I can run queries rapidly.”

Spark isn’t the reason, but it is the solution
SnappyData is pulled in to work on Hybrid Transaction/Analytical Processing use cases because SnappyData is a hybrid database, and in turn they pull Spark in because it’s part of the SnappyData solution.

“All of a sudden it’s not just a Spark API, but what we provide looks like a SQL database itself,” Ramnarayan said.

Machine learning models traditionally learn on historical data search, but parlance is evolving very rapidly so being able to sort and order the model itself incrementally and at near real-time speeds is becoming more and more important, he added. SnappyData is working with the University of Michigan on incrementally augmenting a model on the edge or even inside the cloud, and even venturing into re-tweaking model unsupervised. But that’s “all in the future,” Ramnarayan stated.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Spark Summit 2017. (* Disclosure: DataBricks Inc. sponsored this Spark Summit 2017 segment on SiliconANGLE Media’s theCUBE. Neither DataBricks nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)