The Fast Data Challenge and Picking the Right Database Why One Size Doesn't Fit All with Michael Stonebraker
Interacting with fast data, data that is in motion, is a fundamentally different process than interacting with big data that is at rest. And few businesses have the ability to extract the value of that data when it matters most — at the moment it arrives — because traditional database technology simply hasn't kept pace.
Dr. Michael Stonebraker, Professor at MIT and co-founder of VoltDB, has long held the belief that, without the right database architecture in place, today's organizations run the risk of being left behind in a world that's smarter and faster than what legacy systems can handle.
It’s time to rethink the technology stack needed to enable fast data applications. What’s needed is a framework for making technology choices and architecting fast data applications, the fast data stack. The fast data stack has three levels: data ingestion, real-time analytics and decisions, and data export.
Dr. Michael Stonebraker will share his "one-size-doesn’t-fit-all" perspective when it comes to picking the right tool for the job. He will explain the fast data stack, why traditional RDBMS’s fall short and how a modern in-memory SQL, ACID compliant, database with a scale-out architecture is the right choice for enabling fast data applications. Then watch as John Hugg provides the “proof in the pudding” with a step-by-step review of his Unique Devices application, which performs real-time analytics on fast moving data. It's a representative implementation of the speed layer in the Lambda Architecture with the logic captured in just 30 lines of code.