BEGIN:VCALENDAR VERSION:2.0 PRODID:-//SQLBits/com CALSCALE:GREGORIAN METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VEVENT DTSTART:20120330T095000Z DTEND:20120330T105000Z LOCATION:SQLBits Conference - Bouzy SUMMARY:BigData vs. Data Warehousing DESCRIPTION:

We are looking at the dawn of a new workload: BigData. In this session I will talk about what BigData
is, and which BigData technologies Microsoft are working on. I will talk about how we collaborate with the HADOOP community, and where this is taking our current roadmap. I will also provide some basic introduction to Map/Reduce programming and the mindset you adopt to apply it.


One of the big questions today is: “What will HADOOP do to the world of data warehousing?”. This is
fascinating because BigData and data warehousing interact in a very interesting ways. In the second part of the session, I will share my thoughts on HADOOP’s place in the enterprise architecture.

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BigData vs. Data Warehousing



We are looking at the dawn of a new workload: BigData. In this session I will talk about what BigData
is, and which BigData technologies Microsoft are working on. I will talk about how we collaborate with the HADOOP community, and where this is taking our current roadmap. I will also provide some basic introduction to Map/Reduce programming and the mindset you adopt to apply it.


One of the big questions today is: “What will HADOOP do to the world of data warehousing?”. This is
fascinating because BigData and data warehousing interact in a very interesting ways. In the second part of the session, I will share my thoughts on HADOOP’s place in the enterprise architecture.

Thomas Kejser

Thomas has been working with SQL Server since version 6.5 and performance tuned cubes since OLAP Server 7.0. He has worked on some of the largest cubes in the world. Performing fast loading of warehousing and removing every bottleneck is one of Thomas specialties. He holds a master degree in computer science and has 10+ years of experience with developer coaching and scalable architectures on both OLTP and OLAP systems.

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