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  • Critical essentials of a SAP HANA big data strategy

    Big data presents a big quandary. With data flooding into the enterprise, business leaders are hard pressed to corral that data and turn it into insight. SAP believes HANA and associated big data tools can remedy that situation.

    This handbook is meant to help you create a HANA big data strategy. In the first feature, you'll learn about HANA Vora, a connector between HANA and Hadoop, the open source, Java-based programming framework for large data sets. You'll also learn why open source tools are so important to the HANA big data strategy. Deploying and running HANA or HANA Vora is challenging, and in the second feature, you'll learn best practices for using them in big data projects. The third feature explores important questions that companies need to answer before integrating big data into their business processes.

    As with any technology deployment, business goals come first, technology second. Indeed, a crucial question you need to answer is: What business problems can big data applications solve? This handbook explores what that means for your company and, in turn, what are the key components of your HANA big data strategy.

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  • Six SuccessFactors integration technologies you need to know

    If you're planning a SuccessFactors implementation, learning about integration tools and their best uses should be high on your to-do list. Here's what you need to know. 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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