Time: April 23, 2013 from 6pm to 8:30pm
Location: University of Colorado Denver
Street: 1200 Larimer St North Classroom Building #1539
City/Town: Denver, Colorado
Website or Map: http://www.meetup.com/Data-Sc…
Event Type: free, and, open, to, all
Organized By: Michael Walker
Latest Activity: Mar 29, 2013
For folks unable to attend in-person, register to attend the event and two (2) hours before the event we will email you a link to watch the event via live webcast.
University of Colorado Denver - Tuesday April 23, 2013 @ 6:00pm MST
Large auditorium (170 person capacity) with 20' screen.
Location: CU Denver - North Classroom #1539 - 1200 Larimer Street
Denver, CO 80217-3364 - Map: http://bit.ly/Tyznzg
6:00 - 6:15 Schmooze - Old Chicago Pizza will be served.
6:15 - 8:30 Demonstrate the Spark - Shark Data Analytics Stack on a Hadoop Cluster
8:30 - 9:30 Network at Old Chicago at 14th and Market.
This presentation covers the nuts and bolts of the Spark, Shark and Mesos Data Analytics Stack on a Hadoop Cluster. We will demonstrate capabilities with a data science use-case.
Data scientists need to be able to access and analyze data quickly and easily. The difference between high-value data science and good data science is increasingly about the ability to analyze larger amounts of data at faster speeds. Speed kills in data science and the ability to provide valuable, actionable insights to the client in a timely fashion can mean the difference between competitive advantage and no or little value-added.
One flaw of Hadoop MapReduce is high latency. Considering the growing volume, variety and velocity of data, organizations and data scientists require faster analytical platforms. Put simply, speed kills and Spark gains speed through caching and optimizing the master/node communications.
Spark is an open source cluster computing system that makes data analytics fast. To run programs faster, Spark provides primitives for in-memory cluster computing: your job can load data into memory and query it repeatedly much quicker than with disk-based systems like Hadoop MapReduce.