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Big Data is (at least) Four Different Problems
Wednesday, December 2, 2015 8:00:00 PM UTC - 10:00:00 PM UTC
"Big Data" means different things to different people. To me, it means one of four totally different problems:
Big volumes of data, but "small" analytics. The traditional data warehouse vendors support SQL analytics on very large volumes of data. In this talk, I make a few comments on where I see this market going.
Big analytics on big volumes of data. By big analytics, I mean data clustering, regressions, machine learning, and other much more complex analytics on very large amounts of data. I will explain the various approaches to integrating complex analytics into DBMSs, and discuss which ones seem more promising. In addition, I will explore why Hadoop, in its current form, will not be a player in this market.
Big velocity. By this I mean being able to absorb and process a firehose of incoming data for applications like electronic trading. In this market, the traditional SQL vendors are a non-starter. I will discuss alternatives including complex event processing (CEP), NoSQL and NewSQL systems. I will also make a few comments about the "Internet of Things."
Big Diversity. Many enterprises are faced with integrating a larger and larger number of data sources with diverse data (spreadsheets, web sources, XML, traditional DBMSs). The traditional ETL products do not appear up to the challenges of this new world, and I talk about an alternate way to go.


Michael Stonebraker
Co-Director, Intel Science & Technology Center, MIT
Michael Stonebraker (MS 1966, PhD 1971) has been a pioneer of database research and technology for more than a quarter of a century.
He was awarded the IEEE John Von Neumann award in 2005, and the ACM AM Turing Award - the "Nobel Prize of computing" in 2014. 

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