By Marco Russo
Layout and enforce speedy, scalable and maintainable cubes A real-world consultant to designing cubes with research companies 2008Model dimensions and degree teams in BI improvement StudioImplement protection, drill-through, and MDX calculationsLearn find out how to install, computer screen, and performance-tune your cubeFilled with top practices and helpful tricks and information intimately Microsoft's SQL Server research prone 2008 is an OLAP server that enables clients to investigate enterprise information speedy and simply. although, designing cubes in research providers could be a advanced activity: it is all too effortless to make error early on in improvement that bring about critical difficulties whilst the dice is in creation. studying the easiest practices for dice layout sooner than you begin your undertaking may help you stay away from those difficulties and make sure that your undertaking is a success.This e-book deals functional suggestion on the way to pass approximately designing and development quick, scalable, and maintainable cubes that may meet your clients' specifications and help in making your online business Intelligence venture a success.This publication provides readers perception into the easiest practices for designing and construction Microsoft research companies 2008 cubes. It additionally offers information about server structure, functionality tuning, safeguard, and management of an research prone solution.In this e-book, you are going to tips on how to layout and enforce research providers cubes. ranging from designing a knowledge mart for research prone, in the course of the production of dimensions and degree teams, to placing the dice into creation, we will discover the total of the advance lifecycle.This ebook is a useful advisor for a person who's making plans to take advantage of Microsoft research companies 2008 in a company Intelligence undertaking. What you are going to examine from this publication? construct a knowledge mart compatible to be used with research companies Create and configure an research prone undertaking in enterprise Intelligence improvement StudioUse the measurement Wizard and the measurement Editor to construct dimensionsCreate degree teams and affiliate them with dimensionsAdd calculations to the dice, together with enforcing forex conversion and a date instrument dimensionExplore the safety version, together with size protection and cellphone safeguard, and enforce dynamic securityTune queries to get the very best performanceAutomate processing and partition creationMonitor your dice to determine who is truly utilizing it method it is a functional educational for research companies that exhibits readers tips on how to clear up difficulties mostly encountered whereas designing cubes, and explains which positive factors of research companies paintings good and which may be kept away from. The publication walks during the entire dice improvement lifecycle, from construction dimensions, cubes and calculations to tuning and relocating the dice into creation. Who this booklet is written for? This e-book is geared toward research companies builders who have already got a few adventure yet who are looking to cross into extra element on complicated issues, and who are looking to examine top practices for dice layout.
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Additional resources for Expert Cube Development with Microsoft SQL Server 2008 Analysis Services
The cube will be much faster, for both processing and querying, if we use the right data type for each column. Here we provide a brief table that helps us during the design of the data mart, to choose the best data type for each column type: Fact column type Fastest SQL Server data types Surrogate keys tinyint, smallint, int, bigint Date key int in the format yyyymmdd Integer measures tinyint, smallint, int, bigint Numeric measures smallmoney, money, real, float (Note that decimal and vardecimal require more CPU power to process than money and float types) Distinct count columns tinyint, smallint, int, bigint (If your count column is char, consider either hashing or replacing with surrogate key) Clearly, we should always try to use the smallest data type that will be able to hold any single value within the whole range of values needed by the application.
As views are database objects they inherit two important properties: ° We can configure security for views, and so stop unauthorized access to data very easily. ° Views can belong to a schema. If we are using schemas for the definition of subject areas, we can assign views to subject areas. This will lead to a very clean project where each object belongs to the subject area that is relevant to it. Com This material is copyright and is licensed for the sole use by Mauricio Esquenazi on 21st July 2009 10 Kenmare St.
A junk dimension is simply a dimension that merges together attributes that do not belong anywhere else and share the characteristic of having only a few distinct values each. The main reasons for the use of a junk dimension are: • If we join several small dimensions into a single junk dimension, we will reduce the number of fields in the fact table. For a fact table of several million rows this can represent a significant reduction in the amount of space used and the time needed for cube processing.
Expert Cube Development with Microsoft SQL Server 2008 Analysis Services by Marco Russo