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Ryft’s Big Data Analytics Platform Promises 10GB per sec- or Faster- Performance

Ryft Big Data Analytics 10 Gigabytes SecondRyft, a big bata outfit that has products which offer ‘actionable intelligence from complex data’ has rolled out a new analytics platform that claims to be 200X faster than conventional hardware, in analyzing historical and streaming data simultaneously. Dubbed, Ryft ONE, the new offering promises to provide actionable business insights by simultaneously analyzing up to 48 terabytes of historical and streaming data at an 10 gigabytes/second or faster. It claims to be the only commercial 1U platform capable of doing so. “The loosely coupled, physically distributed compute-and-memory nodes of today’s high-end clusters turn daunting data analytics problems into IO bottlenecks that can slow solution times. The Ryft ONE platform aims to supercharge performance on challenging analytics workloads with a scalable 1U device designed for easy integration into existing server environments,”...
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An Introduction to Recommendation Engines

Introduction What is a Recommendation Engine System Hybrid NetflixI’ve previously written a lot on data mining in the abstract; now, I want to start taking you through some practical applications. Welcome to the fascinating world of the recommendation engine- this post will walk through the concepts, and later posts will teach you how to implement your own. What we will learn: I’ll begin our tour by answering four basic questions: What is a recommendation engine? What is the difference between real life recommendation engine and online recommendation engines? Why should we use recommendation engines? What are the different types of recommendation engines? What is a Recommendation Engine ? Wiki Definition: Recommendation Engines are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that user would give to an item. dataaspirant Definition:  Recommendation Engine is a...
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Using Kafka and YARN for Stream Analytics on Hadoop

9503265951_1246bfa461_kUnderstanding Big Data: Stream Analytics and YARN Real-time stream processing is growing in importance, as businesses need to be able to react faster to events as they that occur. Data that is valuable now may be worthless a few hours later. Use cases include sentiment analysis, monitoring and anomaly detection. With cheap and infinitely scalable storage and compute infrastructure, more and more data flows into the Hadoop cluster. For the first time, the opportunity is ripe to fully leverage that infrastructure and bring real-time processing as close to the data in HDFS as possible, yet isolated from other workloads. This need has been a driver for Hadoop native streaming platforms and a key reason why other streaming solutions, like Storm, fall short. This post motivates critical infrastructure pieces to build...
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Pyramid Analytics Just Tripled Their Customer Base, Again

Pyramid Analytics Business Intelligence Magic Quadrant GrowthPyramid Analytics has reported all round growth in the past year, marked by increased customer base – which has been tripling every year in the last three years, and doubled workforce. “The market is rapidly evolving from departmental BI to more mature, cross-departmental BI for the enterprise,” notes Omri Kohl, co-founder and CEO of Pyramid Analytics. “The responses from our customers gave us a ‘big mover’ advantage in the Garter MQ, validating that our governed data discovery platform offers all users the necessary assurance that the data they are working with provides everyone with the same version of the truth.” The Amsterdam based, global outfit hired finance veteran Micha Ben Chorin as chief financial officer recently. The Magic Quadrant for Business Intelligence and Analytics Platforms, conducted annually, published in February this...
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Hilary Mason: The Cult of the Algorithm is “Marketing Bullshit”

Hilary Mason Machine Learning Algorithms BullshitIt seems not a day goes by without an industry leader remarking that machine learning algorithms are the future of everything. Unfortunately, data scientist extraordinaire Hilary Mason is calling bullshit on the whole affair. Mason is the CEO and founder at Fast Forward Labs, a machine intelligence research company. While speaking to Stacey Higginbotham of GigaOM regarding “new connected products” and how some of them come armed with ML algorithms can “anticipate your needs over time and behave accordingly,” she says: “That’s just a bunch of marketing bullshit.” Quite straightforward. Mason is also the Data Scientist in Residence at Accel, has been the Chief Scientist at bitly and co-founded HackNY. As Higginbotham points out, algorithms have gained a new popularity on account of what it is believed can be done...
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AlchemyAPI Now a Part of IBM Watson, Following Acquisition

AlchemyAPI IBM Watson Deep LearningDeep learning innovator, AlchemyAPI has been acquired by tech giant IBM in a bid to further develop its “next generation cognitive computing applications,” essentially adding to Watson’s deep learning potential. AlchemyAPI’s deep learning platform allows structuring of cognitive-infused applications with advanced data analysis capabilities such as taxonomy categorization, entity and keyword extraction, sentiment analysis and web page cleaning, processing billions of API calls per month across 36 countries in eight languages. “Our ability to draw upon both internal and external sources of innovation, from IBM Research to acquisitions like AlchemyAPI, remain central to our strategy of bringing Watson to new markets, industries and regions,” said Mike Rhodin, senior VP at IBM Watson. Founder and CEO of AlchemyAPI, Elliot Turner said: “We founded AlchemyAPI with the mission of democratizing deep learning...
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