By W. H. Inmon
- The new version of the vintage bestseller that introduced the knowledge warehousing covers new techniques and applied sciences, a lot of that have been pioneered by means of Inmon himself
- In addition to explaining the basics of information warehouse platforms, the e-book covers new themes resembling tools for dealing with unstructured information in an information warehouse and storing information throughout a number of garage media
- Discusses the professionals and cons of relational as opposed to multidimensional layout and the way to degree go back on funding in making plans information warehouse tasks
- Covers complicated subject matters, together with facts tracking and trying out
- Although the booklet contains an additional a hundred pages worthy of invaluable content material, the cost has really been lowered from $65 to $55
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Extra resources for Building the Data Warehouse
Once the current day is measured, it can then be compared to the average-day profile. 27 28 Chapter 1 Of course, the average day changes over time, and it makes sense to track these changes periodically so that long-term system trends can be measured. Summary This chapter has discussed the origins of the data warehouse and the larger architecture into which the data warehouse fits. The architecture has evolved throughout the history of the different stages of information processing. There are four levels of data and processing in the architecture — the operational level, the data warehouse level, the departmental or data mart level, and the individual level.
For example, as previously shown in Figure 2-2, as far as encoding of gender is concerned, it matters little whether data in the warehouse is encoded as m/f or 1/0. What does matter is that regardless of method or source application, warehouse encoding is done consistently. If application data is encoded as X/Y for gender, it is converted as it is moved to the warehouse. The same consideration of consistency applies to all application design issues, such as naming conventions, key structure, measurement of attributes, and physical characteristics of data.
For an insurance company, the applications may be for the processing of auto, life, health, and casualty. The major subject areas of the insurance corporation might be customer, policy, premium, and claim. For a manufacturer, the major subject areas might be product, order, vendor, bill of material, and raw goods. For a retailer, the major subject areas may be product, SKU, sale, vendor, and so forth. Each type of company has its own unique set of subjects. 29 30 Chapter 2 SUBJECT ORIENTATION Operational Data warehouse Auto Customer Life Policy Health Premium Casualty Claim Applications Subjects Figure 2-1 An example of a subject orientation of data.