Wiley Publishing - The Data Warehouse ETL Toolkit

Product Description

* Cowritten by Ralph Kimball, the world's leading data warehousing authority, whose previous books have sold more than 150,000 copies
* Delivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL) process
* Delineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouse
* Offers proven time-saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality

From the Back Cover
The single most authoritative guide on the most difficult phase of building a data warehouse

The extract, transform, and load (ETL) phase of the data warehouse development life cycle is far and away the most difficult, time-consuming, and labor-intensive phase of building a data warehouse. Done right, companies can maximize their use of data storage; if not, they can end up wasting millions of dollars storing obsolete and rarely used data. Bestselling author Ralph Kimball, along with Joe Caserta, shows you how a properly designed ETL system extracts the data from the source systems, enforces data quality and consistency standards, conforms the data so that separate sources can be used together, and finally delivers the data in a presentation-ready format.

Serving as a road map for planning, designing, building, and running the back-room of a data warehouse, this book provides complete coverage of proven, timesaving ETL techniques. Beginning with a quick overview of ETL fundamentals, it then looks at ETL data structures, both relational and dimensional. The authors show how to build useful dimensional structures, providing practical examples of techniques.

Along the way you’ll learn how to:

* Plan and design your ETL system
* Choose the appropriate architecture from the many possible options
* Build the development/test/production suite of ETL processes
* Build a comprehensive data cleaning subsystem
* Tune the overall ETL process for optimum performance

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Wiley Publishing - Mastering Data Warehouse Design

Product Description

* A cutting-edge response to Ralph Kimball's challenge to the data warehouse community that answers some tough questions about the effectiveness of the relational approach to data warehousing
* Written by one of the best-known exponents of the Bill Inmon approach to data warehousing
* Addresses head-on the tough issues raised by Kimball and explains how to choose the best modeling technique for solving common data warehouse design problems
* Weighs the pros and cons of relational vs. dimensional modeling techniques
* Focuses on tough modeling problems, including creating and maintaining keys and modeling calendars, hierarchies, transactions, and data quality


From the Back Cover
At last, a balanced approach to data warehousing that leverages the techniques pioneered by Ralph Kimball and Bill Inmon

Since its groundbreaking inception, the approach to understanding data warehousing has been split into two mindsets: Ralph Kimball, who pioneered the use of dimensional modeling techniques for building the data warehouse, and Bill Inmon, who introduced the Corporate Information Factory and leads those who believe in using relational modeling techniques for the data warehouse. Mastering Data Warehouse Design successfully merges Inmon’s data ware- house design philosophies with Kimball’s data mart design philosophies to provide you with a compelling and complete overview of exactly what is involved in designing and building a sustainable and extensible data warehouse.

Most data warehouse managers, designers, and developers are familiar with the open letter written by Ralph Kimball in 2001 to the data warehouse community in which he challenged those in the Inmon camp to answer some tough questions about the effectiveness of the relational approach. Cowritten by one of the best-known experts of the Inmon approach, Claudia Imhoff, this team of authors addresses head-on the challenging questions raised by Kimball in his letter and offers a how-to guide on the appropriate use of both relational and dimensional modeling in a comprehensive business intelligence environment. In addition, you’ll learn the authors’ take on issues such as:

* Which approach has been found most successful in data warehouse environments at companies spanning virtually all major industrial sectors
* The pros and cons of relational vs. dimensional modeling techniques so developers can decide on the best approach for their projects
* Why the architecture should include a data warehouse built on relational data modeling concepts
* The construction and utilization of keys, the historical nature of the data warehouse, hierarchies, and transactional data
* Technical issues needed to ensure that the data warehouse design meets appropriate performance expectations
* Relational modeling techniques for ensuring optimum data warehouse performance and handling changes to data over time

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Building the Data Warehouse

Product Description

* The new edition of the classic bestseller that launched the data warehousing industry covers new approaches and technologies, many of which have been pioneered by Inmon himself
* In addition to explaining the fundamentals of data warehouse systems, the book covers new topics such as methods for handling unstructured data in a data warehouse and storing data across multiple storage media
* Discusses the pros and cons of relational versus multidimensional design and how to measure return on investment in planning data warehouse projects
* Covers advanced topics, including data monitoring and testing
* Although the book includes an extra 100 pages worth of valuable content, the price has actually been reduced from $65 to $55


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Data Warehouses and OLAP

Product Description
Data warehouses and online analytical processing (OLAP) are emerging key technologies for enterprise decision support systems. They provide sophisticated technologies from data integration, data collection and retrieval, query optimization, and data analysis to advanced user interfaces. New research and technological achievements in the area of data warehousing are implemented in commercial database management systems, and organizations are developing data warehouse systems into their information system infrastructures. Data Warehouses and OLAP: Concepts, Architectures and Solutions covers a wide range of technical, technological, and research issues. It provides theoretical frameworks, presents challenges and their possible solutions, and examines the latest empirical research findings in the area. It is a resource of possible solutions and technologies that can be applied when designing, implementing, and deploying a data warehouse, and assists in the dissemination of knowledge in this field.

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The Datawarehouse Lifecycle Toolkit

Product Description

* Presenting the much-anticipated Second Edition, which boasts nearly 40 percent new and revised coverage, reflecting the latest best practices
* This unparalleled tutorial approach covers everything from planning the data warehouse project to implementing the design and managing the data warehouse environment
* The Kimball Group has streamlined the lifecycle methodology to be more efficient and user-friendly based on their thousands of hours of experience in both consulting and training
* They have also revised various lifecycle topics, including dimensional modeling, data warehouse architecture, ETL, and Business Intelligence
* New sections at the end of every process and techniques chapter feature coverage of managing the effort and reducing risk, assuring quality, estimating considerations, Web site resources, and more

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John Wiley Data Warehousing Fundamentals

Product Description
Geared to IT professionals eager to get into the all-important field of data warehousing, this book explores all topics needed by those who design and implement data warehouses. Readers will learn about planning requirements, architecture, infrastructure, data preparation, information delivery, implementation, and maintenance. They'll also find a wealth of industry examples garnered from the author's 25 years of experience in designing and implementing databases and data warehouse applications for major corporations.
Market: IT Professionals, Consultants.

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John Wiley Sons -The Data Warehouse Toolki(2002)

Product Description
Single most authoritative guide from the inventor of the technique.

* Presents unique modeling techniques for e-commerce, and shows strategies for optimizing performance.
* Companion Web site provides updates on dimensional modeling techniques, links related to sites, and source code where appropriate.

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John Wiley and Sons - IBM Data Warehousing with IBM Business Intelligence Tool

Product Description

* Reviews planning and designing architecture and implementing the data warehouse.
* Includes discussions on how and why to apply IBM tools.
* Offers tips, tricks, and workarounds to ensure maximum performance.
* Companion Web site includes technical notes, product updates, corrections, and links to relevant material and training.

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Building a Data Warehouse - With Examples in SQL Server

Product Description

Building a Data Warehouse: With Examples in SQL Server describes how to build a data warehouse completely from scratch and shows practical examples on how to do it. Author Vincent Rainardi also describes some practical issues he has experienced that developers are likely to encounter in their first data warehousing project, along with solutions and advice. The RDBMS used in the examples is SQL Server; the version will not be an issue as long as the user has SQL Server 2005 or later.


The book is organized as follows. In the beginning of this book (Chapters 1 through 6), you learn how to build a data warehouse, for example, defining the architecture, understanding the methodology, gathering the requirements, designing the data models, and creating the databases. Then in Chapters 7 through 10, you learn how to populate the data warehouse, for example, extracting from source systems, loading the data stores, maintaining data quality, and utilizing the metadata. After you populate the data warehouse, in Chapters 11 through 15, you explore how to present data to users using reports and multidimensional databases and how to use the data in the data warehouse for business intelligence, customer relationship management, and other purposes. Chapters 16 and 17 wrap up the book: After you have built your data warehouse, before it can be released to production, you need to test it thoroughly. After your application is in production, you need to understand how to administer data warehouse operation.
What you’ll learn

* A detailed understanding of what it takes to build a data warehouse
* The implementation code in SQL Server to build the data warehouse
* Dimensional modeling, data extraction methods, data warehouse loading, populating dimension and fact tables, data quality, data warehouse architecture, and database design
* Practical data warehousing applications such as business intelligence reports, analytics applications, and customer relationship management

Who is this book for?

There are three audiences for the book. The first are the people who implement the data warehouse. This could be considered a field guide for them. The second is database users/admins who want to get a good understanding of what it would take to build a data warehouse. Finally, the third audience is managers who must make decisions about aspects of the data warehousing task before them and use the book to learn about these issues.

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