Tagged will be hosting our next meetup, June 19th in san francisco at 6:00 PM.
Tagged's address is:848 Battery StreetSan Francisco, CA 94111
Pizza and hangout:
Our event host, Peter Berger, from Tagged say that pizza *will* be provided for our usual 30 minute hangout preceding the tech talks.
Peter Berger, Director of Strategic Development at Tagged will give a lighting overview of Tagged.
Eldad Farkash is the inventor behind "In-Chip Analytics" and CTO at SiSense will talk about the history of data and in-memory analytics solutions. Precisely: What "In-Chip Analytics" is and how it can help. How Vectorization and Super-Scalar Decompression can turn a simple machine into a data monster. Why SIMD is cool and why you should care
Volkmar Uhlig, founder and CTO of HStreaming will discuss the challenges and approaches of processing Big Data in real-time with millisecond latencies. The amount of newly generated machine data dwarfs the growth of available storage capacity and industries need to rethink how to efficiently handle such data volumes. The current approach of hoarding works in the short term but is inefficient and economically unsustainable. One viable approach is to analyze data while in motion and only persist relevant data in long-term storage after analysis. This approach provides two key benefits: data and insights are always fresh and requires substantially less storage and IO capacity.
James Taylor, from Salesforce.com will speak about: new open source project, Phoenix, a SQL layer over HBase that powers the HBase use cases at Salesforce.com. Phoenix compiles your SQL queries into high performance native HBase calls as opposed to using the more traditional batch-oriented map/reduce framework. This opens the door for SQL over HBase to be used in user-facing interactive applications that require better response times. James will give a short demo to illustrate this point.
Damian Eads, co-founder and Director of Engineering at http://wise.io , will talk about the challenges and approaches to real-time machine learning. Data is most valuable at the moment it is captured, meaning that there is an overwhelming need for ML systems that can do real-time learning and prediction. The increasing volume and velocity of data only compounds this problem, making it vital that our analytics software solutions make effective use of the underlying hardware to keep up with the data onslaught. I'll discuss how we've done this with WiseRF™, and talk about the promise of embeddable ML solutions.
"At Tagged, we make it easy to meet new people through friend suggestions, profile browsing, group interests and much more."
"With over 300 million members and 20 million monthly visitors, Tagged (http://about.tagged.com/) is the place where people can discover hundreds of new friends or just meet that special someone."