Jeffrey Breen

Sponsored by

ThinkBig

Get Your Data Lake Right the First Time

Date: This event took place live on June 14 2016

Presented by: Jeffrey Breen

Duration: Approximately 60 minutes.

Cost: Free

Questions? Please send email to




Watch the webcast in its original format.

Sign in to Register

Description:

No one starts a data lake implementation with the intention to fail. However, one analyst firm estimates 90% of all data lake implementations will end up "useless." While there are myriad reasons why data lake implementations fail, surprisingly few are a result of technology choices. Instead, organizational constructs and accompanying data silos are to blame, alongside the common misperception that legacy system design patterns can be directly ported over to Hadoop.

This webcast shows lessons learned from over a dozen data lake implementations including:

  • How data lakes enable new combinations of data and advanced data products but organizational silos squander the opportunity
  • Why organizations encounter difficulty getting Hadoop systems to perform as well as their database systems and what you can do about it
  • Why focusing on Hadoop for IT cost cutting will ultimately get in the way of building new and better capabilities. We'll teach you how to change the conversation.

Hope is not a strategy. There's a good chance that data lake pitfalls are in your immediate path, so join this webinar to see how top companies have side-stepped these hidden and not-so hidden hazards. Armed with this knowledge, you can begin realizing value in every step of your big data journey.

About Jeffrey Breen, VP, Solutions at Think Big Analytics

Jeffrey Breen is Vice President Solutions for Think Big Analytics, a Teradata company. Jeffrey has over 20 years of hands-on and leadership experience in IT, having served as the Chief Technology Officer of Yankee Group and the start-up Navient Corporation. Jeffrey is well known in the R community for his presentations and tutorials, and his method for mining Twitter for consumer sentiment is featured in Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications from Elsevier. He holds an M.A. in Astronomy from the University of Virginia and a B.A. in Physics and Astronomy & Astrophysics from the University of Pennsylvania.