本视频描述了沃达丰如何利用SAP大数据解决方案Margin Assurance。

本视频介绍了SAP HANA2在不同应用场景中把不同来源的数据统一起来。

原文标题：The Analytic-Transactional Data Platform: Enabling the Real-Time Enterprise

This study examines the need for an integrated transactional and analytical data platform in order to process complex analytic queries as a harmonious data collection. One key capability of such a platform is to enrich transaction processing with the intelligence that comes from immediately relevant business intelligence. Such a platform requires maintaining in one database instance the data that is current and relevant to the transactions in question to facilitate the kind of real-time business intelligence necessary to enrich or even determine business decisions at the point of action. Another requirement is to provide transactional applications with the ability to reference other BI data that may reside in other databases, such as data warehouses, and to permit analytical applications running on data warehouses to enrich their analytics with data from transactional databases and other data collections, including nonrelational data stores. This study describes such a platform, considers the kinds of technology options available in the market today, and projects future developments that will

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enhance this area of functionality.

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The SAP HANA Cloud Platform harnesses the power of the IoT to allow the cubeXX to run interactively. According to SAP’s Vice President of M2M and IoT Engineering, Uwe Kubach, the HANA Cloud Platform enables the processing of large data volumes, which are generated by the cubeXX’s various sensors and equipment on an ongoing basis.

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HANA allows STILL to connect each forklift’s scanning and processing technology to a customers’ enterprise resource planning (ERP) and warehouse management system. In this way, SAP taps into the IoT to help STILL coordinate vehicle data in the cloud in order to track and interrelate information that ranges from records of operating hours to detailed equipment diagnoses.

Uwe Kubach points to STILL and its cubeXX as examples of how SAP partners can take advantage of IoT technology in two ways. “First of all,” he says, “there is the optimization of existing business processes such as maintenance. Secondly, a forklift will no longer be sold as a discreet product, but as a service that is invoiced according to the weight of goods transported."

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原文标题：SAP HANA：Understanding In-Memory Computing

这份PPT谈到了有关SAP HANA的多个主题：

1.HANA是怎样演变而来的？

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2.SAP HANA是什么？

3.SAP HANA的用例。

4.如何通过HANA获得商业洞察？

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SAP HANA is one of the first data management platforms to handle both transactions and analytics in-memory on a single data copy.It converges a state-of-the-art database with advanced data processing services, data integration services, and application services. You gain a single secure environment for all your mission-critical data assets, so you can manage large volumes of structured and unstructured data efficiently to improve total cost of ownership. And, at the same time, you can

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simplify your IT landscape and reduce administrative effort by consolidating multiple workloads

onto SAP HANA. Being able to Run Simple helps you deliver insight into ongoing transactions, so your organization can respond in real time to market change, seizing new opportunities more quickly than your competitors.

And you can develop innovative applications on SAP HANA that combine transactions and analytics and deliver engaging personalized experiences over any device.

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本文档介绍了SAP HANA的使用方法。

原文标题：Enabling the Agile Enterprise Through Unified Data

Analytic data is derived and segregated from transactional data. It requires separate databases with separate schemas — one optimized for transactions, and another optimized for analytics. Data must be moved from operational systems to business intelligence (BI) systems, such as data warehouses, on a periodic basis which results in delays in availability for analytic data, which is usually days or a week old. This keeps analytics from interfering with transactionperformance which can be useful for planning analytics, but not for intra-day decision support.

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Often, application data is segregated by application where each application database has its own schema. Bringing the data together to perform analysis across application spaces is a major project while separate data means many databases to administer. This can also lead to complex administration and operations and schema changes being manually propagated to all related databases. This inflexibility stifles application development, making database applications less agile than they could be.

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