Silicon Valley TDWI Chapters

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TDWI Chapters: Silicon Valley

The purpose of the Silicon Valley Chapter of The Data Warehousing Institute (TDWI) is to enable local BI/DW professionals to:

  • Meet regularly with each other on a regional basis
  • Share best practices in a small group setting
  • Establish a strong network of peers
  • Gain technical advice and career direction

Everyone is welcome. We encourage you to become a member but you do not need to be a TDWI Member to attend.

You may browse previous events using the page buttons at the bottom of the page.

Dear BI/DW Professionals,

We cordially invite you to attend our upcoming TDWI Silicon Valley Chapter meeting on November 11th, 2010. We are encouraging our regular BI/DW professional attendees to invite a business user (or sponsor). Come meet other local professionals, swap business cards, share ideas, and exchange career advice while listening to quality presentations in a vendor-neutral setting, which is the hallmark of TDWI education. See our meeting agenda below. Online Pre-Registration is requested.

When: Thursday, November 11th, 2010 from 4:00 pm – 7:30 pm
Where: Tres Agaves
130 Townsend Street
San Francisco, CA 94107
(2nd Street and Townsend Street)



Click here to register for the next upcoming event


4:00 – 4:30 pm Registration and Networking
4:30 – 4:45 pm Opening remarks by Jeff Oberhauser-Lim, TDWI Silicon Valley Chapter President
4:45 – 5:30 pm Presentation by Brian Dolan, Owner and VP of Product at Discovix, and Owner of Nasrudin Consulting.
As massive data acquisition and storage becomes increasingly affordable, a wide variety of enterprises are employing statisticians to engage in sophisticated data analysis. In this talk we highlight the emerging practice of Magnetic, Agile, Deep (MAD) data analysis as a radical departure from traditional Enterprise Data Warehouses and Business Intelligence. We present design philosophy, techniques and experience in the field providing MAD analytics for businesses and researchers confronted with ever-expanding data sets.

We describe design methodologies that support the agile working style of analysts in these settings. We present data-parallel algorithms for statistical techniques, with a focus on density methods. Finally, we reflect on system features that enable agile design and flexible algorithm development, with lessons for both SQL and MapReduce programming styles.

5:30 – 5:45 pm Q & A Session
5:45 – 7:00 pm Networking Session
7:00 – 7:30 pm Wrap-Up
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