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Ralph Kimball, Margy Ross
Ralph Kimball invented a data warehousing technique called ?dimensional modelling? and popularised it in his first Wiley bestseller The Data Warehouse Toolkit. Since then dimensional modelling has become the most widely accepted technique for data warehouse design. Since the first edition, Kimball has improved on his earlier techniques and created many new ones. In this second edition, he provides a comprehensive collection of all of them, from basic to advanced, and strategies for optimising data warehouse design for common business applications. He includes examples for retail sales, inventory management, procurement, orders and invoices, customer relationship management, accounting, financial services, telecommunication and utilities, health care, insurance and more. He also presents unique modelling techniques for e-commerce and shows strategies for optimising performance. A companion Web site provides updates on dimensional modelling techniques, links to related sites and source code where appropriate.
.NET 3.5 is Microsoft’s largest development software launch since .NET 2.0 and (unlike .NET 3.0) completely replaces all previous .NET versions. A new version of Visual Studio – Visual Studio ‘Orcas’ is being created for the new Framework together with new versions of both the C# and Visual Basic languages. This book deals with this new C# language and provides developers with a complete treatise on the new technology – explaining the importance of all the new features (lambda expressions, LINQ, ASP.NET AJAX, WPF everywhere) and how they integrate into the framework of the previous .NET versions. It is a comprehensively revised and updated version of the author’s previous award-winning titles.
We are visual animals. But before we can see the world in its true splendor, our brains, just like our computers, have to sort and organize raw data, and then transform that data to produce new images of the world. Beginning Python Visualization: Crafting Visual Transformation Scripts discusses turning many types of small data sources into useful visual data. And, you will learn Python as part of the bargain. What you’ll learn Write ten lines of code and present visual information instead of data soup. Set up an open source environment ready for data visualization. Forget Excel: use Python. Learn numerical and textual processing. Draw graphs and plots based on textual and numerical data. Learn how to deal with images. Who this book is for IT personnel, programmers, engineers, and hobbyists interested in acquiring and displaying data from the Web, sensors, economic trends, and even astronomical sources.