BI - Read Me

Who Should Read This Guide
This 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 Know
To 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 are
              We are in the early stage of Business intelligence architecture guide. This guide address architectural and design aspects of Business intelligence. We anticipate gaps in this guide as we are doing release at the very early stage.  This guide is evolving guide and we will incorporate the valuable feedback from the readers.  

Out of Scope
We 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 Guide
This 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.

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.
·         Data Extraction. 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.
·          Data Staging. 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.
·         Data Loading. This chapter examines the challenges faced in loading and provides options to address it. This chapter provides loading guidelines.
·         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

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