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In most cases, the 3NF stage is sufficient for normalization.ĭenormalization, on the other hand, is an optimized procedure used on a normalized database to increase query performance for data analytics. They do not depend on the primary key, so we create two reference tables: Therefore has transitive functional dependency on Department.Īt this stage, we are creating reference tables with foreign keys-similar to what we did with 2NF, but for “adjective relationships” as well.Ĭontinuing with our example,, , and have transitive functional dependency. In the above 1NF example, if we know the we learn the. Therefore, we create the entities “Class” and “Professor-Class” to model the many-to-many relationship.ģNF: This includes 2NF-plus, all fields (columns) cannot have transitive functional dependency (an indirect relationship between columns) from primary key in the table. To extend our 1NF example to 2NF, we include “verb relationships” but not “adjective relationships.” The “Course” column partially depends on the primary key ++. You are solving the question of whether relationships should be one-to-many or many-to-many at this stage. If not, separate such attributes into another entity. (By definition, each cell is identified by the combination of its unique primary key and a column identifier.)įor example, in this model of university classes, each record entry is uniquely identified by the primary key +.ĢNF: This includes 1NF, but all non-key attributes (i.e., those other than the primary key columns) must depend on the primary key. Normalization procedures include 1NF (first normal form), 2NF, 3NF, etc.ġNF: Eliminate repeating groups of attributes into entities and implement a primary key for each entity. This is performed by creating relationships among tables through primary and foreign keys. Normalization is the process of reducing data redundancy while maintaining data integrity. This model carries the actual design blueprint for the database implementation by DBA and database developers. This model describes schema details, columns, data types, constraints, triggers, indexes, replicas, and backup strategy. This is the final stage of a data model which not only relates to a specific database management system, but also states the operating system, storage strategy, data security, and hardware.
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In addition to entity names and entity relationships, the model defines attributes, primary keys, and foreign keys in each entity. It is a staging model and it can be both user- and system-oriented. This model is used for the implementation of the database management system and it focus on the logical structure of the data. It defines entity names and entity relationships but does not focus on technical problems, computer systems, or database management systems. This model is used to describe the conceptual structure of the business data strategy in the initial stage of design.