Data Analysis on Streams
Date: This event took place live on June 12 2014
Presented by: Mikio Braun
Duration: Approximately 60 minutes.
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Hosted By: Ben Lorica
Analyzing real-time data poses special kinds of challenges, such as dealing with large event rates, aggregating activities for millions of objects in parallel, and processing queries with subsecond latency. In addition, the set of available tools and approaches to deal with streaming data is currently highly fragmented.
In this webcast, Mikio Braun will discuss building reliable and efficient solutions for real-time data analysis, including approaches that rely on scaling--both batch-oriented (such as MapReduce), and stream-oriented (such as Apache Storm and Apache Spark). He will also focus on use of approximative algorithms (used heavily in streamdrill) for counting, trending, and outlier detection.
About Mikio Braun
Mikio Braun is co-founder of Streamdrill, a startup focused on approximative approaches for real real-time big data. He holds a Ph.D. in Machine Learning and has worked in research for a number of years, before becoming interested in putting research results into good use in the industry. His current interests focus on anything to do with real-time data analysis, in particular using approximative approaches beyond scaling. You can follow Mikio on Twitter @mikiobraun
About Ben Lorica
Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media, Inc.. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services. He writes regularly about Big Data and Data Science on the O'Reilly Data blog.