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  • A fast data architecture whizzes by traditional data management tools

    New types of information, and the need to immediately access it, are reshaping the big data market into the fast data market. New data management platforms have emerged and are jockeying for acceptance. Continue Reading

  • Ubiquitous IoT devices demand preemptive data management practices

    Trying to get a handle on the future of the internet of things is tantamount to lassoing a wild horse. Sensors, appliances, vehicles, smart personal devices and industrial equipment are just some of the sources of IoT data pouring into the coffers of organizations. One survey projects 21 billion devices will be connected to the IoT in just a few years, while another survey pegs it at 30 billion. And a third study sees the IoT possibly reaching $11 trillion in overall economic value in 10 years. Those prognostications may seem more like pipe dreams, given the IoT's ever-so-slow march toward widespread implementation. Yet on the shoulders of big data systems, IoT deployments are expected to accelerate partly because of the anticipated development of IoT technology platforms that can be bundled for purchase and installed more easily. And that's good news for IT teams saddled with the enormous task of building an infrastructure that can effectively manage and analyze these rapidly growing pools of IoT data.

    This handbook on IoT data management practices examines the challenges that must be addressed on the road to effective IoT management. In the first feature, consultant Andy Hayler advises organizations to bulk up their IT architecture before tackling massive amounts of IoT data. In the second feature, industry veterans involved in industrial IoT projects tell their stories, including a product development vice president who emphasizes the importance of building flexibility into predictive models and a software architecture vice president who says the ultimate goal of IoT data analysis should be to automate industrial processes. And in the third feature, consultant David Loshin proposes four critical steps in formulating all-encompassing IoT data management practices.

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  • Getting up to speed in the data management technology market

    Big data and cloud computing have transformed today's data management technology market. Whether it's by augmenting traditional data warehousing models, allowing small organizations to realize the power of business intelligence (BI), or helping healthcare providers tackle vast stores of electronic health data, these developments are causing customers to rethink the role of data in their organizations.

    In this three-part guide, we examine where your channel business may fit into the areas of data warehousing and BI. First, we look at opportunities coming out of the confluence of big data software and traditional data warehousing systems. Next, we explore how cloud technology has reshaped the BI market in both good and bad ways -- good in that it has made BI more accessible for small and medium-sized businesses; bad in that it has eroded project margins. Finally, we focus in on data management technology in the healthcare vertical, where a demand for data warehouses has created significant hosting opportunities.

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Evaluate Big data management tools Vendors & Products

Weigh the pros and cons of technologies, products and projects you are considering.

  • Hexis Cyber Solutions' HawkEye AP: Product overview

    Expert Dan Sullivan examines the HawkEye AP platform, a big data security analytics product from Hexis Cyber Solutions that can parse hundreds of different data formats. Continue Reading

  • Build vs. buy equation changes, as open source big data tools surge

    The build vs. buy decision is a fluid one, as open source big data tools proliferate. This edition of the Talking Data podcast looks at the new quandary. Continue Reading

  • Spark processing engine gains energy, users

    A fire is catching in the world of big data processing. Since it was first introduced by The Apache Software Foundation a few years ago, the Spark processing engine has been moving throughout the big data ecosystem, latching onto users ripe for change the way a wildfire takes to some vegetation better than others.

    A major appeal of Spark -- which is often paired with the Hadoop framework -- is its speed, especially compared to MapReduce, another Hadoop partner. Of course, the processing engine's youth also means it has areas in need of improvement. Some users struggle to stay on top of Spark updates because the other tools they pair it with don't have the latest version. But the benefits of Spark outweigh the inefficiencies for many users. "It's a stable [technology], and I have no hesitation at all about deploying it," said Peter Crossley, director of product architecture and technology at Webtrends Inc., one of the many users profiled in this e-book chapter written by Executive Editor Craig Stedman.

    Webtrends was an early adopter of Spark and recently expanded the processing engine's role in its big data operations. Other users, like cloud software vendor Xactly Corp., are newer to the technology but are already seeing its benefits. One thing is for sure: Spark has caught the attention of companies that want to process information fast.

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Problem Solve Big data management tools Issues

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  • Choices widen for BI big data tools

    Once upon a time not too long ago, when the term big data was born and bandied about, most companies viewed using big data as just a pipe dream. The processing demands, data storage needs and associated costs to integrate BI big data analytics software were just too great for most organizations to even consider. But that’s all changing. The benefits and competitive advantages afforded by big data analytics are no longer limited to big companies with big budgets.

    In this special edition of Business Information, David Loshin walks readers through the buying process for big data analytics tools. In his first feature, Loshin writes that companies of all sizes now have greater access to big data platforms. Analytics tools are less expensive and easier to use now, to the point where even mainstream business users can manage massive amounts of BI data from multiple sources, make predictions and prescribe solutions.

    But before investing in a BI big data analytics tool, Loshin advises companies to do some self-reflection. It’s not just about the applications; it’s also about a company’s mindset and culture, as Loshin explains in his second feature.

    The decision-making process can be tedious, complex and time-consuming. Look to Loshin's third feature for advice on how to match your company's buying criteria with the right BI big data analytics product. Continue Reading

  • The enterprise data hub poised to become the heart of data management?

    Moving big data can cause network strain, leading some businesses to turn to the enterprise data hub. Continue Reading

  • Overcoming the big data bottleneck caused by data in transit

    By investing in new technology, a research consortium avoids the big headache caused by big data in transit. Continue Reading

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