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While the relation being normalized is a proposed table, it does not yet physically exist as a table, so the physical terms are not quite accurate We use the term relation instead of table, attribute instead of column, and unique identi er instead of primary key For newcomers to normalization, it s only natural to use the more familiar physical terms, but do be aware of the preferred terminology if you seek out additional information or examples from other sources While object names in most DBMSs are not case sensitive, I have shown all table and column names in uppercase for consistency However, I have shown relation and attribute names in mixed case because that is the custom in the industry The normalization process is applied systematically to each user view.

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In the first example, the new columns will have NULL values for existing customers. In the second example, the MIN_QTY column will have the value zero (0) for existing products, which is appropriate. When the ALTER TABLE statement first appeared in SQL implementations, the only major structures within a table were the column definitions, and it was very clear what the ADD clause meant. Since then, tables have grown to include primary and foreign key definitions and constraints, and the ADD clauses for these types of objects specify which type of object is being added. For consistency with these other ADD/DROP clauses, the SQL2 standard includes the optional keyword COLUMN after the keyword ADD. With this addition, the preceding example becomes: Add a minimum inventory-level column to the PRODUCTS table.

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The ALTER TABLE statement can be used to drop one or more columns from an existing table when they are no longer needed. Here is an example that drops the HIRE_DATE column from the SALESREPS table: Drop a column from the SALESREPS table.

The SQL2 standard forces you to issue a separate ALTER TABLE statement if you want to drop several columns, but several of the major DBMS brands allow you to drop multiple columns with a single statement. Note that dropping a column can pose the same kinds of data-integrity issues that were described in 11 for database update operations. For example, if you drop a column that is a primary key in some relationship, the foreign key columns that refer to the dropped column become invalid. A similar problem can arise if you drop a column that is referenced in a check constraint the column that provides the data value for

13:

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At least in the beginning, it is easiest to represent each user view as a two-dimensional table with representative data, as I have done in Figure 1-3 As you work through the normalization process, you will be rewriting existing relations and creating new ones Rewriting user views into relations (tables) with representative data is a tedious and time-consuming process Care must be taken that any sample data used to make decisions during normalization is truly representative of the kinds of data values that will appear in real data As you might expect, poorly constructed sample data often yields a poorly designed database The good news is that, with practice, you will be able to visualize the sample data and avoid the tedium of recording all of it Keep in mind that normalization is intended to remove insert, update, and delete anomalies.

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