Computing: Real-time Results with Big Data through In-Memory
Project GitHub | LinkedIn: Niki Moritz Hao-Wei Matthew Oren
The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Nikita Ivanov is founder and CTO of GridGain Systems, a leading in-memory computing platform. He has over 20 years of experience in software application development. Building HPC and middleware platforms, contributing to the efforts of other startups and notable companies including Adaptec, Visa and BEA Systems.
Nikita was one of the pioneers in using Java technology for server-side middleware development. Hi is an active member of Java middleware community. And contributor to the Java specification.
We came together for a night of networking with Big Data professionals. And learning about leading edge technology that is pushing the envelope of what Big Data can do.
We gave an overview of general in-memory computing principles and the drivers behind it. Some of the topics covered but not limited to were:
- of general in-memory computing principles and the drivers behind it
- popular and emerging use cases for in-memory computing, from financial industry trading platforms to mobile payment processing, online advertising, online/mobile gaming back-ends and more
- foundational concepts and terminology, and considerations around any in-memory solution
- illustration of how a complete in-memory computing stack like the GridGain Data Fabric combines clustering, high performance computing, in-memory data grids, stream processing and Hadoop acceleration into one unified and easy to use platform