Leopard 2A7+, GermanyThe Leopard 2A7+ is a next generation main battle tank (MBT) revealed by Krauss-Maffei Wegmann (KMW) in 2010. The new version leverages the technology of the Leopard 2 MBT and has been adopted by the Bundeswehr (German Army)...
Those of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case.
Two years after its initial announcement and preview release, Lars Bak announced the first stable release of Dart at Devvox in Belgium today. Dart is Google’s new web programming language and platform for developing modern web applications.
Working on Big Data projects with Telefonica Digital, Carsten Hufe and the comSysto-Team started looking for an efficient and affordable way to store and query large amounts of data being delivered in large batches through Apache Hadoop. Our goal was to build a data visualization app for end users issuing different kinds of selective queries on already processed data. Some of the queries were returning large result sets of up to 800.000 JSON documents representing data points for browser visualisation.
Four years ago we announced SPDY, an experimental protocol designed to make the web faster. It has matured quickly since then: it’s been adopted by Chrome, Opera, Firefox and Internet Explorer, dozens of server and middleware vendors, and many large sites. SPDY also became the foundation of the HTTP/2 protocol developed by the IETF, and is continuing to serve as an experimental ground for prototyping and testing new features for HTTP/2.
Every time one of the 1.2 billion people who use Facebook visits the site, they see a completely unique, dynamically generated home page. There are several different applications powering this experience--and others across the site--that require global, real-time data fetching.
Storing and accessing hundreds of petabytes of data is a huge challenge, and we're constantly improving and overhauling our tools to make this as fast and efficient as possible. Today, we are open-sourcing RocksDB, an embeddable, persistent key-value store for fast storage that we built and use here at Facebook.
The big news here – and the headlining feature for CUDA 6 – is that NVIDIA has implemented complete unified memory support within CUDA. The toolkit has possessed unified virtual addressing support since CUDA 4, allowing the disparate x86 and GPU memory pools to be addressed together in a single space. But unified virtual addressing only simplified memory management; it did not get rid of the required explicit memory copying and pinning operations necessary to bring over data to the GPU first before the GPU could work on it.
If you think Python is a snake and ruby is a gemstone, you’re right but not quite when it comes to identifying programming languages. A new infographic provides an encyclopaedic look at various programming languages and their functions.
Google’s seminal paper on Map-Reduce  was the trigger that led to lot of developments in the big data space. Though the Map-Reduce paradigm was known in functional programming literature, the paper provided scalable implementations of the paradigm on a cluster of nodes.