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If you want to use neural networks to build models that can learn from data in human-like ways, but are having trouble figuring out where to start, you may want to check out Ersatz, the name of the new GPU-powered deep learning platform officially unveiled today by Ersatz Labs. The domain of machine learning is advancing very quickly at the moment, driven by humankind’s rather sudden (and entirely insatiable) desire to keep and understand all data. There are also big Read more…
There’s no lack of database choices when it comes to building your next big data analytics platform. Relational data stores, column-oriented databases, in-memory data grids, graph databases, scale-out NewSQL systems, and Hadoop will all get time in the sun. But according to database pioneer Mike Stonebraker, none of these hold a candle to array databases when it comes to running complex analytics on big data sets.
While still in its infancy, the big data technology trend has made a lot of substantial progress since it gained traction at the beginning of this decade. The year 2013 was a big year with advances being made in virtually every quarter of the space. In this feature, we take a look at some of the significant trends that have crossed our desks in the past year — wrapped up and presented to you with a pretty bow. Out with the old, in with the new — it’s the Datanami 2013 Year in Review!
In 1977, IBM researcher John Backus wrote a seminal paper that asked the question: “Can programming be liberated from the von Neumann style?” This week, Micron Technology has announced a new computing architecture called the Automata Processor, which it says answers the Backus question with a definitive “yes!”
The wait for Hadoop 2.0 ended yesterday when the Apache Software Foundation (ASF) announced the availability of the new big data platform. Among the most anticipated features is the new YARN scheduler, which will make it easier for users to run different workloads–such as MapReduce, HBase, SQL, graph analysis, and stream processing–on the same hardware, and all at the same time. Better availability and scalability, and a smoother upgrade process, round out the new platform, as Hadoop creator Doug Cutting explains, but still not everybody is happy with Hadoop.
News In Brief
A global survey of software developers working on big data and advanced analytics projects found that the large majority of respondents require real-time, complex event processing for their applications. The survey, released July 29 by Evans Data Corp. of Santa Cruz, Calif., found that 71 percent of respondents said they require advanced processing more than half the time. “Hadoop’s batch processing model has worked well for several years,” Evans Data CEO Janel Garvin noted in a statement releasing the survey. Read more…
You’ve got to see, smell, feel Beijing’s smog to believe it. Looking out a high window of a tall building, the air is opaque. Despite rules against them, vendors cook and sell food on side streets using charcoal grills. Prevailing winds from the Gobi Desert distribute dust throughout the sprawling capital. The desert dust along with particulates from high-sulfur coal burned in the city’s power plants prompt car owners to brush off their vehicles the way we might clear snow Read more…
A form of artificial intelligence variously referred to as “information compression” or the SP theory of intelligence promises to help solve some of the most vexing challenges associated with increasingly fragmented big data. In a recent article published in the journal IEEE Access, researcher Gerry Wolff of CognitiveResearch.org argues that the human brain in the form of what he calls an “SP machine” could help sift through the mountains of disparate scientific, business, social media and other forms of data Read more…
The big data technology trend is set to make an impact in the battle for the preservation of endangered species, as HP this week announced it’s teamed up with Conservation International to develop what they say is an early warning system for threatened species.
Fifty percent of big data projects fail, Jim Kaskade, CEO of Infochimps, told an audience at the Strata + Hadoop World conference last month. One of the chief reasons that people’s projects fail: their eye is not on the ball. It’s the application, not the data, that should be the primary focus, he says.
This Just In
Clusters have become the workhorse for computational science and engineering research, powering innovation and discovery that advance science and society. They are the base for building today’s rapidly evolving cloud and HPC infrastructures, and are used to solve some of the most complex problems. The challenge to make them scalable, efficient, and more functional requires the joint effort of the cluster community in the areas of cluster system design, management and monitoring, at hardware, system, middleware, and application level.
The San Diego Supercomputer Center (SDSC) at the University of California, San Diego, has formally established a new ‘center of excellence’ to assist researchers in creating workflows to better manage the tremendous amount of data being generated across a wide range of scientific disciplines, from natural sciences to marketing research.
Cloudera, the leader in enterprise analytic data management powered by Apache Hadoop, today announced the industry’s first hands-on data science certification, called Cloudera Certified Professional: Data Scientist (CCP: DS).
Process Stream announced today a partnership with Qlik, a leader in user-driven Business Intelligence (BI). Under the agreement, Process Stream will offer the QlikView Business Discovery platform in combination with its implementation services and business process expertise to the life sciences industry.
Neo Technology, creators of Neo4j, the world’s leading graph database, today announced that Zephyr Health, a fast-growing big data analytics platform for companies in the life sciences industry, is using Neo4j to power its high-speed, scalable platform that turns data into actionable information. Neo4j enables Zephyr Health users to be ‘their own data scientist’ with the ability to discover new connections between data from disparate sources with a graph database that can rapidly adapt to a changing business.