Building the data warehouse by william inmon pdf download

About the Author Bill Inmonthe father of the data warehouse concept, has written wilpiam books on data management, data warehouse, design review, and management of data processing. Bill has published more than articles in many trade journals. Bill founded and took public Prism Solutions. Bill holds two software patents. Articles, white papers, presentations, and much more material can be found on his Web site, www. Permissions Request permission to reuse content from this site.

Evolution of Decision Support Systems. The Data Warehouse Environment. The Data Warehouse and Design. Granularity in the Data Warehouse. The Data Warehouse and Technology. Other software that needs to be considered is the interface software that provides transformation and metadata capability such as PRISM Solutions Warehouse Manager.

A final piece of software that is important is the software needed for changed data capture, such as that provided by PRISM Solutions. If the hardware and software platforms are either much too big or are much too little for the amount of data that will reside in the data warehouse, then no iterative development should occur until the fit is properly made. If the hardware and dbms software are much too large for the data warehouse, the costs of building and running the data warehouse will be exorbitant.

Even though performance will be no problem, development and operational costs and finances will be a problem. Conversely, if the hardware and dbms software are much too small for the size of the data warehouse, then performance of operations and the ultimate end user satisfaction with the data warehouse will suffer. At the outset it is important that there be a comfortable fit between the data warehouse and the hardware and dbms software that will house and manipulate the warehouse.

In order to determine what the fit is like, the data needs to be sized. The sizing does not need to be precise. If anything, the sizing needs to err on the size of being too large, rather than too small. But a rough sizing of the data to be housed in the data warehouse at this point can save much grief at a later point in time if in fact there is not a comfortable fit between the data warehouse data and the environment it is built in.

The estimate of the data warehouse data should be in terms of order of magnitude. Of course, one of the issues that relates to the volume of data in the data warehouse is that of the level of granularity of data. If too much data is likely to be built into the data warehouse, the level of granularity can always be adjusted so that the physical volume of data is reduced. Figure 4 shows the typical means by which those informational requirements are identified and collected.

Existing reports can usually be gathered quickly and inexpensively. In most cases the information displayed on these reports is easily discerned. But old reports represent yesterday's requirements and the underlying calculation of information may not be obvious ay all.

Spreadsheets are able to be easily gathered by asking the DSS analyst community. Like standard reports, the information on spreadsheets is able to be discerned easily. Through EIS and other channels there is usually quite a bit of other useful information analysis that has been created by the organization.

This information is usually very unstructured and very informal although in many cases it is still very valuable information. Typically through interviews or JAD sessions, the end user can tell what the informational needs of the organization are. Unfortunately JAD sessions require an enormous amount of energy to conduct and assimilate.

Furthermore, the effectiveness of JAD sessions depends in no small part on the imagination and spontaneity of the end user participating in the session. In any case, gathering the obvious and easily accessed informational needs of the organization should be done and should be factored into the data warehouse data model prior to the development of the first iteration of the data warehouse. The first issue of design and planning for the first iteration of the data warehouse to be developed is exactly how much data is to be loaded and what variety of data is to be loaded.

It is almost never the case that huge amounts of data are loaded as a result of the first iteration. There are many different ways to cut down the size of data without losing its effectiveness.

Figure 5 illustrates a few of those ways. There are practically speaking, an infinite number of ways to subset data. What is a meaningful subset depends entirely on who the first user of the first iteration of the data warehouse will be. The data architect needs to ask the question - who will be the first user of the first iteration of data. Now, knowing whom the first user is, what data would be meaningful to them? There is a careful line to be walked here.

On the one hand the data architect must take care not to put too much data in the first iteration of development. On the other hand the data architect must take care not to include so little data that the spontaneity of discovery by the DSS analyst is limited. But there are some classical functional areas that over the years have borne more fruit than others.

Figure 6 depicts those functional arenas. Finance data tends to speak directly to the executive. It tends to get right to the heart of the corporation. In addition, finance data is relatively smaller, in terms of volume, than other types of data for most corporations. Phillip Front rated it really liked it Aug 23, Inmon Snippet view — User Review — Flag as inappropriate this is about the data warehousing.

Granularity in the Data Warehouse. Published October 1st by Wiley first published November 30th They do not add warehiuse further clarity to the topic or the situation. J suggest the authors to have a look at the Kimball books. Building the Data Warehouse, 4th Edition W.

Read more Read less. If designed and built right, data warehouses can provide significant freedom of access to data, thereby delivering enormous benefits to any organization. Articles, white papers, presentations, and much more material can be found on his Web site, www. If the topic will mention about a simple data flow from A to B then it will refer to a diagram The book covers the data warehousing field completely — giving a degree view to a reader.

Return to Book Page. This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are as essential for the working of basic functionalities of the website.

We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. Articles, white papers, presentations, and much more material can be found on his Web site, www. Bill holds two software patents. Overall, I got nothing from that book. I found interesting the chapter about how to manage unstructured data.

Jun 15, Mike rated it really liked it. This needs to be your starting point if you want to understand data warehousing; everything you need to know from the fundamental principles onwards is contained within its pages!

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