Strata Conference + Hadoop World 2013 is looming on the horizon and pacing to be the largest gathering of Big Data professionals on the globe. As co-hosts with O’Reilly, we have seen the conference thrive, grow, and are excited about the upcoming Oct. 28 – 30 event!
Below you will find a listing of all the ways you can engage with Cloudera throughout the conference (all speakers are Cloudera employees unless otherwise indicated):
- Cloudera co-founder and Chief Strategy Officer Mike Olson will explain how Apache Hadoop and Cloudera are transforming the way organizations think about their data.
Join us February 26 – 28 at Strata Santa Clara and learn how Cloudera has developed the de facto Platform for Big Data. Visit Cloudera in booth 701 to hear from our team in a series of presentations and partner-integrated demonstrations – agenda coming soon. We will also be hosting several celebrated authors of the Big Data community who will be available to sign copies of their published works and converse with you about the Big Data environment and specific projects within the Big Data space.
Our video animation factory has been busy lately. The embedded player below contains our two latest ones stitched together:
Get Started with Hadoop Using Cloudera Enterprise, Part 1
To be a proactive industry leader, you must be able to consume critical data as it is produced, analyze it quickly and act on your results. Before Hadoop there was no scalable, cost-effective way to do this with Big Data.
This post was contributed by Bob Gourley, editor, CTOvision.com.
You are no doubt aware of the interesting situation we face with data today: The amount of data being created is growing faster than humans can analyze, but fast analysis over data can help humanity solve some very tough challenges. This fact is moving the globe towards new “Big Data” solutions.
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Up to this point, we’ve described our reasons for using Hadoop and Hive on our neural recordings (Part I), the reasons why the analyses of these recordings are interesting from a scientific perspective, and detailed descriptions of our implementation of these analyses using Apache Hadoop and Apache Hive (Part II). The last part of this story cuts straight to the results and then discusses important lessons we learned along the way and future goals for improving the analysis framework we’ve built so far.