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Your Big Data training experience shouldn't be limited to deep-dive tutorials and great Big Data classes. You also deserve some high-level inspiration and vision on the future of Big Data technologies. At Big Data TechCon we've assembled a trio of keynote speakers who will inspire and challenge you, as well as take you into some of the most advanced Big Data companies in the world. Buckle your seat belts and prepare to be transported!

 

Tuesday, April 9, 9:45 AM — 10:45 AM • Dr. Michael Stonebraker

Big Data is (at least) Four Different Problems
Our ability to mine and analyze 'Big Data' has yet to catch up to our ability to generate and collect it. Moreover "Big Data" and "Big Analytics" mean different things to different people. This keynote from one of Big Data's rock stars unpacks the Big Data problem into four distinct domains: Big volumes of data with SQL analytics, Big volumes of data with complex analytics, Big velocity, Big diversity.


Dr. Michael StonebrakerDr. Michael Stonebraker has been a pioneer of data base research and technology for more than a quarter of a century. He is widely recognized as one of the world's foremost experts in database technology and is noted for his insight in operating systems and expert systems.

Over his career, Mike has been both a professor and leading architect for prototype development. He was the main architect of the INGRES relational DBMS, the object-relational DBMS, POSTGRES, and the federated data system, Mariposa. All three prototypes were developed at the University of California at Berkeley where he was a Professor of Computer Science for 25. He is the founder of three successful Silicon Valley startups, whose objective was to commercialize these prototypes. Additionally, Mike has authored scores of research papers on database technology, operating systems and the architecture of system software services.

Mike is presently an Adjunct Professor of Computer Science at M.I.T., where he is working on a variety of future-generation data-oriented projects. He is co-director of the new Intel Science and Technology Center at MIT CSAIL focusing on Big Data Analytics.

Mike is also a co-founder of eight software startups, including Streambase, Vertica Systems, VoltDB, Goby, and Paradigm4. He was awarded the prestigious ACM System Software Award in 1992 for his work on INGRES, the first annual Innovation award by the ACM SIGMOD special interest group in 1994, and the IEEE John Von Neumann award in 2005. Mike was elected to the National Academy of Engineering in 1997 and to the American Academy of Arts and Sciences in 2010.

 

Tuesday, April 9, 4:30 PM — 5:30 PM • Oscar Boykin, Ph.D.

Addition in the large: Simple counts and not-so-simple counts
Counting is the first mathematics most of us learn, but we move beyond. In this keynote we talk about the power of counting, some of the properties that make it work well at scale, and then we'll move on to large scale data on a memory budget. How can something like a count tell us how many distinct visitors we had in a period of time? How can we make and use approximate counts to build scalable analytics? We'll take a tour of state of the art approximation techniques and see examples of the kinds of data-products they enable. Finally, we'll discuss how to implement these schemes in real-time and on Hadoop.


Oscar BoykinOscar Boykin, Ph.D., is a Staff Software Engineer at Twitter. He is also co-author of Scalding, Twitter's library for large-scale Hadoop programming. At Twitter, he is building systems for real-time Big Data with applications to ad targeting. Before joining Twitter, he was an Assistant Professor at the University of Florida in Electrical and Computer Engineering where his research was focused on large-scale distributed systems.

 

Wednesday, April 10, 10:00 AM — 11:00 AM • Adam Laiacano

Improve, don't abuse: Use Big Data for your customers, not against them
Social networks are able to collect large amounts of activity data from their user and customer base. As Big Data professionals, we conduct experiments on custom data sets to measure the effectiveness of our products or advertising methodologies. Since a social network is effectively useless without an active community, our companies owe it to their users to create new and better products based on this information. Learn how our data analysis and predictive analytics must take a different approach than Big Data in fields like finance, medicine, and defense.


Adam Laiacano Adam Laiacano is a Data Scientist and Engineer at Tumblr, a blogging network with over 90 million blogs, where he's responsible for collecting and analyzing large volumes of data to gain a better understanding of trends and activity within the Tumblr community. He holds a Bachelor of Science degree in Electrical Engineering from Northeastern University, and designed signal detection systems for low-power atomic clocks before joining Tumblr.



 

 

A BZ Media Production