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Colton Green
Colton Green

Kimball Ross - The Data Warehouse Toolkit 2nd...


The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including:




Kimball Ross - The Data Warehouse Toolkit 2nd...


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By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.


Ralph Kimball invented a data warehousing technique called "dimensional modeling" and popularized it in his first Wiley book, The Data Warehouse Toolkit. Since this book was first published in 1996, dimensional modeling has become the most widely accepted technique for data warehouse design. Over the past 5 years, Kimball has improved on his earlier techniques and created many new ones. In this second edition, he provides a comprehensive collection of all of these techniques, from basic to advanced.


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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


This textbook covers various artificial intelligence, data science, and analytics topics. Analytics, Data Science, and Artificial Intelligence is one of the advanced data science books that explores the connection between different parts of an organization and the impact of decisions on the overall system. Also, the authors touch on ethical considerations in data science with guidance on handling potential decision-making issues. This book explores several case studies and how different technologies can help improve systems performance. Analytics, Data Science, and Artificial Intelligence is a valuable book for professionals and students looking to develop skills across various data science and management areas.


The Data Warehouse Toolkit, 3rd Edition (9781118530801) Ralph Kimball invented a data warehousing technique called "dimensional modeling" and popularized it in his first Wiley book, The Data Warehouse Toolkit. Since this book was first published in 1996, dimensional modeling has become the most widely accepted technique for data warehouse design. Over the past 10 years, Kimball has improved on his earlier techniques and created many new ones. In this 3rd edition, he will provide a comprehensive collection of all of these techniques, from basic to advanced.


The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775) Complete coverage of best practices from data warehouse project inception through on-going program management. Updates industry best practices to be in sync with current recommendations of Kimball Group. Streamlines the lifecycle methodology to be more efficient and user-friendly


The Data Warehouse ETL Toolkit (9780764567575) shows data warehouse developers how to effectively manage the ETL (Extract, Transform, Load) phase of the data warehouse development lifecycle. The authors show developers the best methods for extracting data from scattered sources throughout the enterprise, removing obsolete, redundant, and inaccurate data, transforming the remaining data into correctly formatted data structures, and then physically loading them into the data warehouse.


RALPH KIMBALL, PhD, has been a leading visionary in the data warehouse and business intelligence industry since 1982. The Data Warehouse Toolkit book series have been bestsellers since 1996. MARGY ROSS is President of the Kimball Group and the coauthor of five Toolkit books with Ralph Kimball. She has focused exclusively on data warehousing and business intelligence for more than 30 years.


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To build the lung and ovarian cancer clinical data warehouse, preprocessing operations were applied to data. Data processing was the most important and time-consuming part of the process of designing the data warehouse, which included various processes, such as filtering, cleaning and transforming the data, to ensure better quality and accurate results. In this study, the data warehouse was developed with Microsoft SQL Server 2012. First, the data were extracted from the source system. SQL server integration services (SSIS) were used for the extract-transform-load (ETL) process. After the elimination of wrong and inconsistent data, the data were transformed and loaded into the data warehouse.


The data are stored in a fact constellation schema model using a multi-dimensional modeling approach to perform OLAP operations. The multi-dimensional data model is based on concepts such as cube, dimension, and hierarchy. The fact constellation schema model used is easy to understand and appropriate for query performance. As seen in Figure 4, the fact table contains keys representing each of the dimension tables. The lung and ovarian cancer data warehouse model created using the Microsoft SQL Server has 14 dimension tables and 2 fact tables that contain the following: (1) complications, diagnoses, treatments, X-ray results, and X-ray anomalies for each cancer type, (2) patient information, diet history, dietary data and time dimensions shared by both fact tables, (3) one lung cancer fact table, and (4) one ovarian cancer fact table.


In this study, a clinical data warehouse was designed for lung and ovarian cancers with data collected from the NCI. The main goal of this research was to analyze and store data regarding two different cancer types in one single data warehouse. We ensured that the data warehouse would conform to the subject-oriented, integrated, time-variant and nonvolatile conditions of the data warehouse approach presented by [18]. Data were integrated through preprocessing, transformation, and selection operations. In the design of the data warehouse, the fact constellation schema model, a multi-dimensional data modeling approach, was used. This model succeeded in responding to complex queries, and the analysis of data was facilitated by using OLAP cubes and viewing multi-level data details.


Indisputably, the fact constellation schema is the most challenging data warehouse design architecture. For the first time, real medical data were used combining two different cancer types using fact constellation modelling to extract semantically useful information to improve the decision-making process for cancer patients. The present approach also provides the ability to access data in the data warehouse using complex queries because dimensional attributes are shared by a number of fact tables, unlike the star and snowflake schemas. The fact constellation schema is capable of dealing with complex systems because the relationships between fact and dimensional tables are more easily understood. The medical field requires advanced data analytical processing by studying real critical medical data and this work addresses this need.


Warren Thornthwaite is famous for being the author of 2 great DW books: Microsoft Data Warehouse Toolkit (with Joy Mundy) and Data Warehouse Lifecycle Toolkit (with Kimball, Ross and Mundy). He is also well known for being a member of the Kimball Group, teaching 2 Kimball University classes: DW Lifecycle in Depth (with Margy Ross) and Microsoft DW in Depth (with Joy Mundy). He wrote a lot of DW articles for Kimball Group, and therefore an author of the Kimball Group Reader book. Warren has the experience of building data warehouses since 1980. He was the Program Director for Data Warehousing course in Stanford University. He worked for Metaphor for 8 years, from 184 to 1991. He was the BI Manager for Microsoft Web TV for 5 years (1997-2002). Warren is the owner of InfoDynamics, which has now joined the Kimball Group. His LinkedIn profile is here. His Kimball Group profile is here. His Amazon profile is here.


As an introduction to the course the basic data models for both operative databases/On Line Transactional Processing (OLTP) and data varehuse/On Line Analytical Processing (OLAP) are described.The focus of the course is how to design, describe, implement, and evaluate data warehouses for different line of businesses/organisations. From a business point of view the main problem in data warehouse design is that most companies/organizations have lots of data, but these data are not structured in order to give information for decision support.


I recently had the opportunity to author the editor's note for TechNet Magazine. I have to say that, being a developer, addressing an audience of IT professionals was a bit daunting. Both disciplines are vital to any business, but many times their paths only cross when something is broken. However, I believe that when it comes to the management of data, both developers and IT professionals need to be involved up front in planning solutions. Given that the theme of that particular TechNet Magazine issue was business intelligence and that the theme of this issue of MSDN Magazine is data, I'll address some of the main points I made in that editor's note but more from the developer perspective. 041b061a72


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