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Two schools of thought reign as to the best way to organize OLTP data into a data warehouse: the summary table approach and the star schema approach The following subsections take a look at each approach, along with the benefits and drawbacks of each
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Inmon originally developed the summary table data warehouse architecture This data warehouse approach involves storing data not only in detail form, but also in summary tables so that analysis processes do not have to summarize the same data continually This is an obvious violation of the principles of normalization, but because the data is historical and therefore is not expected to change after it is stored the data anomalies (insert, update, and delete) that drive the need for normalization simply don t exist Figure 12-1 shows the summary table data warehouse architecture Data from one or more operational data sources (databases or flat file systems) is periodically moved into the data warehouse database A major key to success is determining the appropriate level of detail that must be carried in the database and anticipating the necessary levels of summarization Using Acme Industries as an example,
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Operational Data Source 1
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Operational Data Source 2
Lightly Summarized Data Table(s)
End-user Analysis Tools
Operational Data Source 3
Detailed Data Table(s)
Figure 12-1 Summary table data warehouse architecture
if the subject of the data warehouse is sales, it may be necessary to keep every single invoice, or it may be necessary to keep only those invoices that exceed a certain amount or perhaps only those that contain certain products If requirements are not understood, it is unlikely that the data warehouse project will be successful Failure rates of data warehouse projects are higher than most other types of IT projects, and the most common cause of failure is poorly defined requirements In terms of summarization, we might summarize the transactions by month in one summary table and by product in another At the next level of summarization, we might summarize the months by quarter in one table and the products by department in another An end user (the person using the analysis tools to obtain results from the OLAP database) might look at sales by quarter and notice that one particular quarter doesn t look quite right The user can expand the quarter of concern and examine the data for months within it This process is known as drilling down to more detailed levels The user may then choose a particular month of interest and drill down to the detailed transactions for that month
Databases: A Beginner s Guide
The metadata (data about data) shown in Figure 12-1 is very important and, unfortunately, often a missing link Ideally, the metadata defines every data item in the data warehouse, along with sufficient information so its source can be tracked all the way back to the original source data in the operational database The biggest challenge with metadata is that, lacking standards, each vendor of data warehouse tools has stored metadata in its own way When multiple analysis tools are in use, metadata must usually be loaded into each one of them using proprietary formats For end-user analysis tools (also called OLAP tools or business intelligence tools), not only are tools embedded in major relational database products such as SQL Server and Oracle, but literally dozens of specialized commercial products are available, including Business Objects (now owned by SAP), Cognos (an IBM company), Actuate, Hyperion (now owned by Oracle), and many more
Star Schema Data Warehouse Architecture
Kimball developed a specialized database structure known as the star schema for storing data warehouse data His contribution to OLAP data storage is significant Red Brick, the first DBMS devoted exclusively to OLAP data storage, used the star schema In addition, Red Brick offered SQL extensions specifically for data analysis, including moving averages, this year versus last year, market share, and ranking Informix acquired Red Brick s technology, and later IBM acquired Informix, so IBM now markets the Red Brick technology as part of its data warehouse solution Figure 12-2 shows the basic architecture of a data warehouse using the star schema The star schema uses a single detailed data table, called a fact table, surrounded by supporting reference data tables called dimension tables, forming a starlike pattern Compared with the summary table data warehouse architecture, the fact table replaces the detailed data tables, and the dimension tables logically replace the summary tables Aside from the primary key, each attribute in the fact table must be either a fact (a metric that can be summarized) or a foreign key to a dimension table Keep in mind that facts must be additive, such as quantities, scores, time intervals, and currency amounts A new star schema is constructed for each additional fact table Dimension tables have a one-to-many relationship with the fact table, with the primary key of the dimension table appearing as a foreign key in the fact table However, dimension tables are not necessarily normalized because they may have an entire hierarchy, such as layers of an organization or different subcomponents of time, compressed into a single table The dimension tables may or may not contain summary information, such as totals, but they generally should not contain facts Using our prior Acme Industries sales example, the fact table would contain the invoices from the table, and typical dimension tables would be time (days, months,
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