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Part I
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Query
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SELECT Dimensions that will appear in results Aggregated fact that will appear in results product.category, product.product, SUM( order_facts.order_dollars ) AS "ORDER DOLLARS" FROM day, Tables in the query product, order_facts WHERE Dimensions are used to filter the results Surrogate key columns are used to join the tables together day.month_name = "January" AND day.year = 2009 AND order_facts.day_key = day.day_key AND order_facts.product_key = product.product_key GROUP BY Dimensions specify scope of SQL SUM() aggregation product.category, product.product ORDER BY Dimensions control sorting of results product.category, product.product
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CATEGORY ============== Packaging Packaging Packaging Packaging Packaging Packaging Pens Pens Pens PRODUCT ================= Box Large Box Medium Box Small Clasp Letter Envelope #10 Envelope Bubble Gel Pen Black Gel Pen Blue Silver Pen ORDER DOLLARS ========= 1,100.00 1,271.00 2,220.00 7,503.00 5,872.00 6,708.00 987.00 1,980.00 2,206.00
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Each row summarizes numerous order lines from the fact table
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Dimension values provide context
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The fact, aggregated
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Figure 1-6 Querying the star schema
Browsing Dimensions
An often overlooked, but equally important, form of interaction with a dimensional schema is the browse query. Browsing is the act of exploring the data within a dimension. The results of browse queries appear as reference data, and may make useful reports. A browse activity may also be an exploratory precursor to a larger query against the fact table.
1 Analytic Databases and Dimensional Design 15
SQL Query
SELECT DISTINCT product.category FROM product ORDER By product.category
Query Results
CATEGORY ======== . . . Fasteners Folders Packaging Pens Measurement Notebooks Storage . . .
SQL Query
SELECT DISTINCT product.category, product.product FROM product WHERE product.category = "Packaging" ORDER BY product.product
Query Results
CATEGORY ======== Packaging Packaging Packaging Packaging Packaging Packaging PRODUCT ======= Box - Large Box - Medium Box - Small Clasp Letter Envelope #10 Envelope Bubble
Figure 1-7
Browse queries and their results
Like a query against a fact table, a browse query is not limited to studying information at the level of detail stored in the database. Instead, queries may browse for distinct combinations of attribute values. Figure 1-7 shows some queries that browse the product dimension. The first browse in Figure 1-7 simply fetches a list of product categories. The second browse seeks the list of products within a specific category. Browse queries may return many attributes from within a dimension; some tools support browsing in a grid-like interface. The browse query is important in several respects. It may serve as the basis for the selection of query predicates, or filters, for a query that involves a fact table. A browse query may also allow users to explore the relationship between dimension values. This kind of browsing may be considered when making decisions about how to group attributes into dimensions, as discussed in 6.
Guiding Principles
The remainder of this book covers a wealth of dimensional design techniques which you can use to describe any business process. Sometimes it will be useful to understand the reason some of these techniques have been developed. Other times, it may be necessary for you to
Part I
PART I
Fundamentals
choose from some design options. Two simple guiding principles drive these decisions: accuracy and performance. It may seem obvious, but it is important to consider the accuracy of any given design. The questions that will be asked of an operational system can be determined in advance, and remain consistent over time, but analytic questions always lead to new questions. They will change over time, sometimes dramatically so. Designers must pay close attention to how a dimensional schema represents facts. Is it possible that they will be aggregated in ways that do not make sense Is there a design alternative that can prevent such a situation Of equal importance is the performance of the schema. An analytic design may offer little value over an operational design if it cannot produce timely results. Dimensional designs are very good at providing a rapid response to a wide range of unanticipated questions. There will be times, however, when a basic design may not be able to serve important business needs efficiently. The performance profile of a solution may drive the decision to provide information in more than one format, as will be seen throughout this book.
Summary
Dimensional modeling is a design approach optimized for analytic systems. A dimensional model captures how a process is measured. Data elements that represent measurements are called facts. Data elements that provide context for measurements are called dimensions. These elements are grouped into dimension tables and fact tables. Implemented in a relational database, the design is called a star schema. The dimension tables in a star schema employ surrogate keys, enabling the analytic system to respond to changes in operational data in its own way. The granular facts in a star schema can be queried at various levels of detail, aggregated according to desired dimensional context. Exploring the details within a dimension is referred to as browsing. This chapter has only begun to introduce the fundamentals of dimensional design. After a discussion of architectures in 2, 3 will return to the basics of dimensional design.
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