If your goal is to provide data to an analyst rather than a data scientist, what’s the best way to deliver analytics? There are 70+ BI tools in the market and a dozen or more SQL- or OLAP-on-Hadoop open source projects. Mark Madsen and Shant Hovsepian outline the trade-offs between a number of architectures that provide self-service access to data and discuss the pros and cons of architectures, deployment strategies, and examples of BI on big data.
Mark Madsen is a Fellow at Teradata, where he’s responsible for understanding, forecasting, and defining analytics ecosystems and architectures. Previously, he was CEO of Third Nature, where he advised companies on data strategy and technology planning, and vendors on product management. Mark has designed analysis, machine learning, data collection, and data management infrastructure for companies worldwide.
Shant Hovsepian is a cofounder and CTO of Arcadia Data, where he’s responsible for the company’s long-term innovation and technical direction. Previously, Shant was an early member of the engineering team at Teradata, which he joined through the acquisition of Aster Data, and he interned at Google, where he worked on optimizing the AdWords database. His experience includes everything from Linux kernel programming and database optimization to visualization. He started his first lemonade stand at the age of four and ran a small IT consulting business in high school. Shant studied computer science at UCLA, where he had publications in top-tier computer systems conferences.
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