Building the data warehouse can be time consuming There can be a high initial cost Longer time for start-up Specialist team required Business Dimensional Lifecycle Definition and Main Principles: This approach, defined by Ralph Kimball, is bottoms, up where data marts are created to provide reporting. The data architecture is a collection of confirmed dimensions and confirmed facts that are shared between facts in two or more data marts. The data integration requirements for this bottom up approach includes data integration requirements for individual business areas. Business Dimensional Lifecycle Pros and Cons Pros: Takes less time to build the data warehouse Low initial cost with fairly predictable subsequent costs Fast initial set up Only a generalist team is required Cons: Maintenance can be difficult, redundant and subject to revisions In the top-down approach, unlike the bottom-up approach, there is an enterprise data warehouse, relational tools, normalized data model, complexity in design, and a discrete time frame. In the bottom-up approach, unlike the top-down approach, there are dimensional tools, process orientation and a slowly changing time frame. But, how the data is modeled, loaded and stored is different.
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Author: William H. Inmon, Claudia Imhoff, and Ryan Sousa might be the perfect personal floatation device. In this book, the authors collaborate to examine the components of a corporate information factory and explain how they should work together. Generally, the authors show the lifecycle of data as it moves from transaction systems to an enterprise data warehouse and from the warehouse through various means into the hands of decision support system DSS analysts.
This page book is conceptually divided into four parts. The first two chapters define the corporate information factory concept.
The next twelve chapters examine each component of the corporate information factory architecture in detail. In the last three chapters, the authors discuss the management of a corporate information factory. Inmon, Imhoff, and Sousa clearly have the professional stature and knowledge to clearly define the industry standard terminology that surrounds the data warehouse. In my reading, I found their classification of decision support system users into tourists, farmers, explorers, and miners to be a wonderfully useful analogy.
It is particularly helpful in gaining an understanding of why an enterprise might consider an exploration or data mining data warehouse. I was fortunate to have a forward thinking manager who led us through a chapter-by- chapter discussion of this work. It was a good way to ensure that everyone within our data architecture organization was mentally and literally on the same page about operational data stores, data warehouses, and data marts.
Indeed with enough time, free subscription cards, or a fast Internet connection, one might be able to glean the information in this book from a variety of sources. But, the book brings the ideas together much more conveniently. Highly technical readers will be disappointed at the lack of detail on some aspects of the architecture. This is simply not the book to provide information on dimensional data modeling techniques or the pros and cons of various ETL tools.
From a management perspective a more serious criticism may be that this book does not cover any role that Enterprise Application Integration EAI might play or its merits as the cornerstone of an alternative architecture.
Some of the illustrations and graphics are also a bit busy and confusing.
Data Warehouse Architecture: Inmon CIF, Kimball Dimensional or Linstedt Data Vault?
ETL Processes. To bring data from the transaction system, a process called ETL is used. ETL stands for extract, transform and load. ETL process consolidates data, transform it into a specific standard format and load it into a single repository called enterprise data warehouse, or EDW. ETL processes can run as a batch process periodically or a transaction-based for near real-time data. ETL process is referred as data integration or data services. Data in the enterprise data warehouse is captured at a very lowest level of detail.
William H. Inmon was born July 20, in San Diego, California. In he founded Pine Cone Systems, which was renamed Ambeo later on. In , he created a corporate information factory web site for his consulting business. Inmon promotes building, usage, and maintenance of data warehouses and related topics.