Big Data technology is enabling incredible new business opportunities and cost savings for enterprises. These powerful new techniques require radical changes to existing data management architectures and novel approaches for data analytics. In this presentation, I will discuss some of the new architectural patterns for Big Data and innovative use cases IBM has addressed with Big Data technology. One of the most exciting topics in Big Data is the rapidly emerging field of stream computing that enables continuous in-memory processing of vast quantities of data with low latency. Streaming technology gives developers the freedom to perform computations naturally as data arrives versus being forced into the traditional store and process approaches. At IBM, our research and development teams have worked for nearly a decade to create a stream computing platform that is extremely fast, efficient, and agile for developers -- I will describe how it works and what makes it so compelling. Finally, I'll talk about the challenges and opportunities we see over the next several years in Big Data.
Dr. James Giles is an IBM Distinguished Engineer for Big Data products and Engineering Lead for IBM's real-time stream processing platform, InfoSphere Streams. Before that, Jim managed the Advanced Platform Services group at the IBM T. J. Watson Research Center. There, Jim and his team developed the technology for the System S stream processing prototype which is now the basis for the IBM InfoSphere Streams product. Jim joined IBM in 2000 as a Research Staff Member and led research and development in content distribution, policy management, autonomic computing, and security. He received his Ph.D. in electrical and computer engineering from the University of Illinois at Urbana-Champaign where he studied covert communications in data networks. Jim has several patents and is the recipient of an IBM Corporate Award for his work on stream computing.