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By using the Oracle Exadata Database Machine as a data warehouse platform you have a balanced, high performance hardware configuration. This paper focuses on the other two corner stones, data modeling and data loading, providing a set of best practices and examples for deploying a data warehouse on the Oracle Exadata Database Machine.
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The construction of a data model is one of the more difficult tasks of software engineering and is often pivotal to the success or failure of a project. Many factors determine the effectiveness of a data model. In this white paper, industry expert Michael Blaha covers the Top 10 pitfalls to avoid - from both the strategy and detail perspective.
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Read this paper to learn all the factors you need to consider when choosing a data modeling tool. You will learn about the different model types and how each tool measures up to the demanding needs of your company today and in the future. This paper will lay out all the information you need to make a clear decision on data modeling today.
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The following white paper explores the top 7 considerations for implementing a data visualization solution in your enterprise. Learn how data visualization can give way to far deeper insights, better decision making, and much more.
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Read on to find details about SAPs BusinessObjects Predictive Analysis, including how the NBA used HANA to help cater to stat-hungry fans.
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This paper analyzes the issues of conventional data warehouse design process and explains how this practice can be improved using a business-model-driven process in support of effective Business Intelligence.
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Cloud Computing is an emerging market, and there exist an increasing number of companies that are implementing the cloud model, products and services. Read this white paper to find out more about this exciting new technology.
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This paper explains how modeling information architecture (IA) can help reduce the costs associated with data management. Read this now and learn about the benefits of implementing IA and how Sybase's option offers modeling support for database design and enterprise architecture.
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Whether or not analytics should become an integral part of an organization’s planning and decision-making seems to be beyond question However, at what level, for what purpose and how to go about deploying analytics are questions that each organization needs to answer for itself. These questions are the focus of this paper.
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In the following paper, we briefly describe, and illustrate from examples, what we believe are the “Top 10” mistakes of data mining, in terms of frequency and seriousness. Most are basic, though a few are subtle. All have, when undetected, left analysts worse off than if they’d never looked at their data.
