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Business IntelligenceArchitecture and Design

Guide

Summary: Welcome to Microsoft Patterns & Practices Business intelligence architecture anddesign guide. This guide gives you prescriptive guidance on how to overcome architectural

challenges and design issues when building Business Intelligence solutions using Microsoftplatform.

Microsoft Confidential. 2006 Microsoft Corporation. All rights reserved. These materials areconfidential to and maintained as a trade secret by Microsoft Corporation. Information in thesematerials is restricted to Microsoft authorized recipients only. Any use, distribution or publicdiscussion of, and any feedback to, these materials is subject to the terms of the attachedlicense. By providing any feedback on these materials to Microsoft, you agree to the terms of thatlicense.

Microsoft Confidential. 2006 Microsoft Corporation. All rights reserved. By using orproviding feedback on these materials, you agree to the attached license agreement.

Please provide feedback at BI Feedback Alias.
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Who Should Read This GuideThis guide is intended for software architects and developers who are developing

Business Intelligence applications on the Microsoft .NET Framework using SQL Server

2005 - Integration Services, Analysis Services, Reporting Services and Microsoft Office.

What You Must KnowTo most effectively use this guidance, you should already have experience using SQL

Server 2005, .NET development techniques and technologies. You should be familiar

with general distributed application architecture challenges, and, if you have already

implemented business intelligence solutions, you should know your own application

architecture and deployment pattern.

Where we areWe are in the early stage of Business intelligence architecture guide. This guide

address architectural and design aspects of Business intelligence. We anticipate gaps in thisguide as we are doing release at the very early stage. This guide is evolving guide and we willincorporate the valuable feedback from the readers.

Out of ScopeWe will not be providing any business solutions nor discuss any domain specific business

problems. Concepts discussed in this guide are well suited for Microsoft BI solution framework.

This guide will not dwell deep into all the Microsoft product feature sets rather focus on core

functionalities.

What Is in This GuideThis guide consists of the following chapters, each of which deals with a specific issue

relevant to business intelligence. Each chapter is designed to be read, in whole or in part,

according to your needs.




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In Release 4.0 (Final) we will be covering the following chapters.

BI Framework Data Extraction

Data Staging

Data Transformations and Data Quality

Data Loading

Dimensional Modeling

Data Partitions

Online Analytical Processing (OLAP)

Data Mining

Introduction. This chapter examines the challenges you might face when designing

and building business intelligence applications. It then discusses some of the high-

level architectural challenges, solutions and provides guidance to help you determine

and build an architecture right for your application. This guide will introduce you a BI

frame to help you organize and prioritize challenges. Use the design guidelines in this

chapter to learn practices, principles, patterns, and anti-patterns that will help you to

make informed choices.

DataExtraction. This chapter examines the various extraction models and

techniques. It also covers the various options available for doing extraction in

Microsoft Product Stack. Address the most frequent challenges faced during

extraction like delta detection, high volume etc.

DataStaging. This chapter examines the thoughts behind stage or not to stage and

types of staging

Data Transformations and Data Quality: This chapter examines the various data

quality problems and proposes the solutions. Common transformations techniques

and guidelines are detailed in this chapter.

DataLoading. This chapter examines the challenges faced in loading and provides

options to address it. This chapter provides loading guidelines.



Meta data, Security,Performance & Operations

Part-I

Data

Source

Part-II

DataExtraction

Data QualityDataTransformationDataLoadingDataStaging

Part-III

Dimensional

ModelingDataPartitions

Part-IV

OLAPDataMining

Part-V

HostFormatPresentation

SOURCE

SOURCE

INTEGRATION

INTEGRATION STORAGE

STORAGE ANALYSIS

ANALYSIS

PRESENTATION

PRESENTATION
http://mswikis/biwiki/Pages/Chapter%201%20Introduction.aspxhttp://mswikis/biwiki/Pages/Chapter%201%20Introduction.aspxhttp://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxhttp://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxhttp://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxhttp://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxhttp://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxhttp://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxmailto:[email protected]?subject=BIFEEDBACK:http://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxhttp://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxhttp://mswikis/biwiki/Pages/Chapter%204%20Data%20Integration.aspxmailto:[email protected]?subject=BIFEEDBACK:http://mswikis/biwiki/Pages/Chapter%201%20Introduction.aspx


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Dimensional Modeling: This chapter details the dimensional modeling techniques

on design guidelines for facts, dimensions. It explains the common guidelines and

solutions for the general design problems.

Data Partitions: This chapter details the techniques that can be adopted for

designing the partitions. The basic advantage of partitions is to reduce the loading

time and so the availability is increased, and boosting the query performance

Online Analytical Processing (OLAP): This chapter details the concept and

implementation details of OLAP. It has the guidelines that can be considered for

various custom measures, storage structures, aggregations and querying the multi

dimensional data.

Data Mining: This chapter details the basic concept of Data Mining, need of data

mining, techniques, guidelines and customer scenarios for choosing the right data

mining model

Collaborators and Reviewers

Special thanks to our reviewers:

Special thanks to our external reviewer:

Thanks to our editors and release team:

Thanks to our test team:

Thanks to our product management:

Feedback on the GuideWe have made every effort to ensure the accuracy of this guide. We are particularly

interested in feedback regarding the following:

Technical issues specific to recommendations

Usefulness and usability issues Writing and editing issues

Email your valuable feedback to [email protected]. We sincerely appreciate yourvaluable feedback.


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