As Hadoop continues its ascent into enterprise applications, traditional analytic boundaries and data processing methodologies are rapidly becoming obsolete. ETL has long been a constraining aspect of the data analytics pipeline, often limiting the types of data and questions an analyst could ask. With advances throughout the analytics ecosystem, why does this fundamental problem persist?
Although there have been several SQL-on-Hadoop tools introduced recently (a concept that Hadapt pioneered in 2009), these tools still rely on ETL technology (or MapReduce jobs) to structure raw data into a SQL-queryable format. Join Daniel Abadi, Hadapt Chief Scientist, and Matt Grace, Objective Logistics CTO, to hear how Hadapt continues to lead the innovation curve with the Data-Driven Schema and Multi-Structured Tables, dramatically improving time-to-insight and depth of analytic possibility.
Welcome to the world of Schemaless SQL.
This session is sponsored by Hadapt
Daniel is a recognized expert in database systems and one of the inventors of Hadapt’s patent-pending Adaptive Analytical Platform™.
Daniel received his PhD from Massachusetts Institute of Technology, where his dissertation on column-store database systems led to the founding of Vertica (recently acquired by Hewlett Packard). He is a recipient of a Churchill Scholarship, an NSF CAREER Award, a Sloan Research Fellowship, the 2008 SIGMOD Jim Gray Doctoral Dissertation Award, and the 2007 VLDB best paper award.
In addition to serving as a faculty member in Yale University’s Computer Science department, he also serves as Chief Scientist at Hadapt. He blogs at DBMS Musings and tweets at @daniel_abadi.
Matt has been a programmer since the age of 13. After starting a marketing aggregation software company in his teens he attended the University of Massachusetts for computer science. He has built financial software for State Street Global Advisors, schedule optimization software for ClickSoftware, and most recently spent 4 years as a director of product management at Oracle. Matt likes building stuff (recently things of the culinary sort) and is interested in mathematics, machine learning, non-linear optimization, literature, and studying new languages.
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