SnappyData + Spark = Real Time Analytics, Machine Learning, Streaming, OLTP

Topic:

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 SnappyData is innovating and renovating Spark's core underpinnings to make it suitable for real time operational analytics?

• How we helped create a unified platform that supports real time analytics, transactions, streaming and machine learning in a single consistent data store?

• Approximate query processing which is the first real attempt at reducing the time to insights when working with big data

• Demo

Bio

Suds Menon is responsible for engineering, marketing, venture funding and juggles all operational aspects of SnappyData. At Pivotal, Suds was the Head of Products for all the Real Time and Big Data products at Pivotal and was named as a member of the technical leadership team. Prior to that he led GemFire engineering for 8+ years, both at Pivotal and VMware, overseeing the transformation of the product from a disruptive product to a mainstream widely used and supported product within the industry. He holds several patents in the area of distributed systems. He is a frequent speaker at in-memory and distributed data conferences. He has 20+ years of experience in the software industry and holds a Bachelors of Engg in Computers(Pune University) and an MBA (Babson) in Entrepreneurship