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Columns have unique names within the same table. Each column has a domain (or data type) which defines the allowed values in the column. All rows in a table have the same set of columns. This definition does not preclude columns having sets or relations as values, e.g. nested tables. This is the major difference to first normal form.
A relation is in first normal form if and only if no attribute domain has relations as elements. [1] Or more informally, that no table column can have tables as values. Database normalization is the process of representing a database in terms of relations in standard normal forms, where first normal is a minimal requirement.
If a table in 5NF has one primary key column and N attributes, representing the same information in 6NF will require N tables; multi-field updates to a single conceptual record will require updates to multiple tables; and inserts and deletes will similarly require operations across multiple tables.
"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.
Title Authors ----- ----- SQL Examples and Guide 4 The Joy of SQL 1 An Introduction to SQL 2 Pitfalls of SQL 1 Under the precondition that isbn is the only common column name of the two tables and that a column named title only exists in the Book table, one could re-write the query above in the following form:
A table (called the referencing table) can refer to a column (or a group of columns) in another table (the referenced table) by using a foreign key. The referenced column(s) in the referenced table must be under a unique constraint, such as a primary key. Also, self-references are possible (not fully implemented in MS SQL Server though [5]).
A relation can always be decomposed in third normal form, that is, the relation R is rewritten to projections R 1, ..., R n whose join is equal to the original relation. Further, this decomposition does not lose any functional dependency , in the sense that every functional dependency on R can be derived from the functional dependencies that ...
High-cardinality refers to columns with values that are very uncommon or unique. High-cardinality column values are typically identification numbers, email addresses, or user names. An example of a data table column with high-cardinality would be a USERS table with a column named USER_ID. This column would contain unique values of 1-n. Each ...