The video presentation below comes from our friends at the San Francisco Python Meetup group. The talk discusses how AdRoll uses Python to squeeze every last bit of performance out of a single high-end server for the purpose of interactive analysis of terabyte-scale data sets.
Bits are bits. Whether you are searching for whales in audio clips or trying to predict hospitalization rates based on insurance claims, the process is the same: clean the data, generate features, build a model, and iterate.
In this edition of insideBIGDATA’s Data Science 101 series, I’m going to offer up a short instructional video describing the use of the popular unsupervised learning algorithm, k-means clustering.
“As InfiniBand is getting used in scientific computing environments, there is a big demand to harness its benefits for enterprise environments for handling big data and analytics. This talk will focus on high-performance and scalable designs of Hadoop using native RDMA support of InfiniBand and RoCE. Designs for various components in Hadoop (such as HDFS, MapReduce, RPC, and HBASE) and their benefits based on the RDMA package for Apache Hadoop will be presented. RDMA-based design for scalable Memcached (used in Web 2.0) and the associated benefits will be presented.”
Experfy, based at the Harvard Innovation Lab, announced that it has launched a paradigm-changing, online marketplace that will allow industry leaders to solve their Big Data talent needs. Enterprises now have a central platform for on-demand hiring of vetted experts with algorithmic skills and domain knowledge, primarily for short-term projects related to data, analytics and business intelligence.
In this video from GTC 2014, Todd Mostak from MapD demonstrates the company’s GPU-powered in-memory relational database software for Big Data. The Cambridge, Mass., based startup has built a high-speed GPU in-memory database that brings interactivity to big data. It can, for example, track more than a billion tweets worldwide at a time – and provide real-time visual analysis of the data. MapD was also announced as the winner of the GPU Technology Conference’s Early Stage Challenge this year, and they will be coming home with a cool $100,000 check.
In this video from the GPU Technology Conference 2014, Ami Gal from Sqream Technologies describes the company’s innovative Big Data processing technology. “Can you compare the technology of today with the technology of tomorrow? Yes, with SQream Technologies you can. This is because SQream Technologies uses GPUs to capture, store and process Big Data within seconds, resulting in 100x faster insights. Big Data analytics, once considered unattainable, can now be achieved in a matter of seconds with SQream’s hassle-free, robust analytic database.”
I’ve been monitoring an interesting discussion on the Big Data and Analytics group over on LinkedIn – “Is there a difference between big data and small data?” It is an interesting question, one that I’ve heard before during my travels down in the trenches as I explore our industry.