In attempting to work on Hadoop-based data, data scientists face two bad options: use Hadoop indirectly by engaging in a slow and error-prone back-and-forth with "data engineers" who translate your needs into Hadoop programs, or use Hadoop directly by using unfamiliar and unproductive command-line tools that are difficult to master.
Fortunately, a newer breed of Hadoop-based tools is emerging which solve this dilemma. These tools allow you to build an environment for "big data science" which is both self-serve and productive. In this webinar, we present some concrete tips for building such an environment. Attendees will gain practical insights into the processes and systems that enable the rapid iterations required by big data practitioners and their stakeholders.
Raymie Stata, CEO, Altiscale
Tim Matteson, Co-Founder, Data Science Central