Data warehousing fundamentals for IT professionals / Paulraj Ponniah.—2nd ed. p. cm. Previous ed. published under title: Data warehousing fundamentals. Textbook: 1. Data Warehousing Fundamentals for IT Professionals by Ponniah, ISBN: Course Length: This is a semester long 4 credit hour . DATA WAREHOUSING FUNDAMENTALS FOR IT PROFESSIONALS Second Edition PAULRAJ PONNIAH DATA WAREHOUSING FUNDAMENTALS FOR I.
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Data Warehousing Fundamentals: A Comprehensive Guide for IT If professional advice or other expert assistance is required, the services of. Data warehousing fundamentals for IT professionals / Paulraj Ponniah.—2nd ed. p. cm. Previous ed. published under title: Data warehousing. Data Warehousing Fundamentals for it Professionals, Second Edition. Author(s). Paulraj Ponniah. First published Print ISBN
About this book Cutting-edge content and guidance from a data warehousing expert—now expanded to reflect field trends Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field. The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery.
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How do I view solution manuals on my smartphone? You can download our homework help app on iOS or Android to access solutions manuals on your mobile device. Business intelligence BI , therefore, is a broad group of applications and technologies.
First, the term refers to the systems and technologies for gathering, cleansing, consolidating, and storing corporate data. Next, business intelligence relates to the tools, techniques, and applications for analyzing the stored data. The Gartner Group popularized BI as an umbrella term to include concepts and methods to improve business decision making by fact-based support systems.
In this environment data from multiple operational systems are extracted, integrated, cleansed, transformed and stored as information in specially designed repositories. Information to Knowledge. In this environment analytical tools are made available to users to access and analyze the information content in the specially designed repositories and turn information into knowledge. Again, using this information with sophisticated tools for proper decision making is equally challenging.
Therefore, the trend is to consider these as two distinct environments for corporate BI.
Vendors also tend to specialize in tools appropriate for these two distinct environments. However, the two environments are complementary and need to work together. Figure shows the two complementary environments, the data warehousing environment, which transforms data into information, and the analytical environment, which produces knowledge from information.
As we proceed from chapter to chapter, we will keep expanding and intensifying our discussion of these two environments.
In spite of tons of data accumulated by enterprises over the past decades, every enterprise is caught in the middle of an information crisis. Information needed for strategic decision making is not readily available.
All the past attempts by IT to provide strategic information have been failures. This was mainly because IT has been trying to provide strategic information from operational systems. Informational systems are different from the traditional operational systems.
Operational systems are not designed for strategic information. We need a new type of computing environment to provide strategic information. The data warehouse promises to be this new computing environment.
Data warehousing is the viable solution. There is a compelling need for data warehousing in every enterprise.
The challenges faced in early data warehouse implementations led the movement towards maturity. The notion of business intelligence for an enterprise has evolved as an umbrella concept embracing data warehousing and analytics to transform data into information and information into knowledge.
What do we mean by strategic information? Do you agree that a typical retail store collects huge volumes of data through its operational systems?
Name three types of transaction data likely to be collected by a retail store in large volumes during its daily operations. Examine the opportunities that can be provided by strategic information for a medical center.
Why were all the past attempts by IT to provide strategic information failures? The OLAP approach is used to analyze multidimensional data from multiple sources and perspectives.
OLTP systems emphasize very fast query processing and maintaining data integrity in multi-access environments. For OLTP systems, effectiveness is measured by the number of transactions per second. OLTP databases contain detailed and current data. The schema used to store transactional databases is the entity model usually 3NF. Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes.
Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future. These systems are also used for customer relationship management CRM. History[ edit ] The concept of data warehousing dates back to the late s  when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse".
In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments.
In larger corporations, it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data.
The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems usually referred to as legacy systems , was typically in part replicated for each environment.
Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from " data marts " that was tailored for ready access by users.
Key developments in early years of data warehousing: s — General Mills and Dartmouth College , in a joint research project, develop the terms dimensions and facts.