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Preface
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What this book covers
Chapter 1, Data Warehouse Design, explains the rst steps in thinking and designing a
BI system. As the rst steps, a developer needs to design the data warehouse (DW) and
needs an understanding of the key concepts of the design and methodologies to create
the data warehouse.
Chapter 2, SQL Server Analysis Services Muldimensional Cube Development, explains
how an OLAP muldimensional structure is required to provide fast query response and
aggregated data from a data warehouse. In this chapter, readers will learn what OLAP
provides and how to design OLAP with Microso SSAS Muldimensional.
Chapter 3, Tabular Model Development of SQL Server Analysis Services, explains that SSAS
Tabular is a new method of presenng data in Microso BI 2012 and is very useful in small
BI systems and when a developer wants to create POC. In this chapter, the reader will learn
about SSAS Tabular and how to use it in BI projects.
Chapter 4, ETL with Integraon Services, describes how ETL is an operaon of transferring
and integrang data from source systems into the data warehouse. ETL needs to be done
on a scheduled basis. In this chapter, readers learn how to think about ETL processes and
use SSIS to perform data transfers.
Chapter 5, Master Data Management, guides readers on how to manage reference data.
Master Data Management (MDM) is very crical in all integrated systems, especially in BI and
data warehouse. In this chapter, the reader will learn how to use Master Data Services (MDS)
to implement an MDM soluon.
Chapter 6, Data Quality and Data Cleansing, explains that data quality is one of the biggest
concerns of database systems. The data should be cleansed to be reliable through the data
warehouse. In this chapter, readers will learn about data cleansing and how to use Data
Quality Services (DQS), which is one of the new services of SQL Server 2012, to apply data
cleansing on data warehouse.
Chapter 7, Data Mining – Descripve Models in SSAS, provides a descripve model on
historical events. In this chapter, readers will understand data mining concepts and how to
use data mining algorithms to understand the relaonship between historical data, and how
to analyze it using Microso technologies.
Chapter 8, Idenfying Data Paerns – Predicve Models in SSAS, focuses on predicng future
outcomes based on a paern recognized in the exisng data. In this chapter, readers will
become familiar with algorithms that help in predicon, and how to use them and customize
them with parameters. Users will also understand how to compare models together to nd
the best algorithm for the case.
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