Meetup at Klout in San Francisco

Get ready for another siiiiiick lineup of speakers, and some amazing experience stories from one of the hottest companies around: Klout

Topics in the hopper: Storm, Brickhouse, Big Data for Business Guys +++. More details to follow.

Jerome Banks from Klout will talk about Brickhouse:

"Klout is proud to announce the initial release of Brickhouse (https://github.com/klout/brickhouse), an open source project extending Hadoop (http://hadoop.apache.org/) and Hive (http://hive.apache.org/) with a collection of useful user-defined-functions. Its aim is to make the Hive Big Data developer more productive, and to enable scalable and robust dataflows. Klout makes use of many of the projects in the open source Big Data community such as Hadoop, HBase (http://hbase.apache.org/) and Hive. We want to contribute the tools we’ve developed and found useful to the open-source community"

James Ladd co-founder of Vertascale is sliding in with a talk on big data for the business guy:

"Big data," according to The Big Data Manifesto, "is the definitive source of competitive advantage." Yet while big data is undoubtedly the source -- it is a competitive advantage only if it is readily accessible to decision-makers. The industry's open secret is that "time-to-business-insight" remains unacceptably long. Open source provides real-time building blocks for engineering teams -- who measure real-time answers in seconds. But the business guy remains locked out. James saw these issues first hand as Principal Product Manager of Search at eBay where led the charge on Hadoop. This lighting talk collects real-world experiences that James has called upon in developing a real-time data app for the business guy.

Ted Dunning, Chief Application Architect, MapR Technologies will present "Real-time Storm on Hadoop":

"Hadoop is great. But it isn’t great for real-time. Storm is great. But it doesn’t handle ad hoc analysis of years of data. Adding a NoSQL solution to the mix doesn’t necessarily make things simpler, either. Each of these systems handles part of the problem. So how can we handle the need for real-time analysis of data with horizons over years of data? I will define the problem and describe how real-time analysis can mesh with long-time analysis. Then I will show concrete examples of how you can combine Storm and Hadoop to get reliable and accurate real-time analytics. My talk will include architecture, algorithms and a demo."