In today's rapid-paced business environment, you need faster business insights to keep you competitive. Since the advent of big data, there's been an explosion of analytics frameworks both inside and outside of the Hadoop ecosystem.
To meet the different SLAs and processing needs of multiple analytics engines, data driven enterprises have deployed many different specialized clusters, resulting in replication of large data sets attached to Hadoop clusters. A common object storage data lake, coupled with on-demand compute resources, offers a more flexible alternative to faster business insights.
Join Red Hat and Silicon Valley Data Science in this webinar to learn how to:
- Re-architect the relationship between processing and storage.
- Provide data scientists with flexible, cost-efficient resources for their disparate analytics jobs.
- Increase speed to implementation for analytics workloads by using a common object storage data lake.
- Brent Compton, director, Storage Solution Architectures, Red Hat
- Justin Pront, senior solution architect, Silicon Valley Data Science
- Kyle Bader, senior solution architect, Red Hat
Date: March 1, 2017
Time: 12:00 p.m. EST
Length: 1 hour