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Figure 7-5 A snowflake schema
Avoiding the Snowflake
Snowflaking a dimension is similar to a process called normalization, which guides the design of operational systems. This technique was developed to ensure referential integrity of data in operational systems, which support a wide variety of simultaneous transactions that are highly granular. An analytic database does not share this usage pattern, and referential integrity can be enforced by the ETL process. Normalization is therefore not necessary. In fact, modeling the relationships between dimension attributes detracts from usability, complicates ETL, and may even disrupt performance. That said, there are some good reasons you may wish to model a snowflake, as you will see later in this chapter.
Normalization Is Useful in an Operational Setting
Entity-relationship (ER) modeling is often used to design databases that support operational systems, or OLTP (online transaction processing) systems. This form of modeling places a heavy emphasis on capturing the relationships between attributes in much the same way a snowflake does. Through the process of normalization, redundancy is systematically driven out of the data model. Repeating groups are moved to their own tables, and designers ensure
7 Hierarchies and Snowflakes 159
Normalized or Denormalized
You may have noticed that this chapter avoids referring to a star schema as denormalized and a snowflake as normalized. That s because these terms do not clearly map to the dimensional world. Even in the world of operational systems, they are vague. There are actually several normal forms, known as first normal form, second normal form, and so forth. Each form results from the removal of a specific type of redundancy, such as repeating groups or partially dependent attributes. In an ER model, designers strive to achieve a level known as third normal form (or 3NF), but there are further possible normal forms. While it may be useful to describe one dimensional design as more normalized than another, labeling a star schema as denormalized and a snowflake as normalized oversimplifies matters. In a star, for example, dimension attributes do not repeat in fact tables. Instead, they are represented by foreign keys. This represents a degree of normalization, although most stars do not map cleanly to one of the standard forms. Moreover, the use of denormalized when describing a star implies that the design started out as normalized. Most designs are not produced in such a manner. Not normalized would be a better description. Similarly, it is also imprecise to refer to a snowflake as normalized. The snowflake in Figure 7-5, for example, might not meet the standards of third normal form. Address information would probably need to be removed from the customer table; fiscal periods would need to be separated from years. For more information on the mechanics of normalization, consult the Further Reading section at the end of this chapter.
that each attribute is fully dependent on its table s unique identifier. Relationships between attributes are made explicit, exposed as primary key/foreign key relationships, or joins. In an operational setting, the principles of normalization are applied for very practical reasons. As described in 1, Analytic Databases and Dimensional Design operational systems support the full range of transaction types insert, update, delete, query. Each transaction tends to be focused on individual records, rather than a large group. A large number of these transactions may take place simultaneously. In servicing these transactions, the database must satisfy a set of principles often known as ACID principles (atomic, consistent, isolated, and durable). At the same time, there must be a minimal impact on performance. The principles of normalization help the RDBMS achieve these objectives, maintaining data integrity with maximum efficiency. An update to the name of a brand, for example, need only adjust a single row in a brand table. Extensive resources are not required to support the ability to roll back a transaction before it is completed; only a single row is impacted. More importantly, while this transaction is taking place, the RDBMS need not lock every row of the product table that shares the brand in question. Other users are free to access and update individual products, even while information about the brand is being changed. Data integrity is guarded, since each data element resides in a single place. An incidental benefit also accrues: storing each brand once saves some space.
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