There are two ways of designing a physical data base model. The first way is to normalize the data model in order to minimize the data redudancy. The second way is to denormalize the data model in order to empower the data analysis.
Database normalization or simply normalisation, is the process of organizing the columns (attributes) and tables (relations) of a relational database to minimize data redundancy.
Normalization involves decomposing a table into less redundant (and smaller) tables without losing information, and then linking the data back together by defining foreign keys in the old table referencing the primary keys of the new ones. The objective is to isolate data so that additions, deletions, and modifications of an attribute can be made in just one table and then propagated through the rest of the database using the defined foreign keys.
Edgar F. Codd, the inventor of the relational model (RM), introduced the concept of normalization and what we now know as the First normal form (1NF) in 1970. Codd went on to define the Second normal form (2NF) and Third normal form (3NF) in 1971, and Codd and Raymond F. Boyce defined the Boyce-Codd Normal Form (BCNF) in 1974. Informally, a relational database table is often described as “normalized” if it meets Third Normal Form. Most 3NF tables are free of insertion, update, and deletion anomalies.
A typical example of normalization is that an entity’s unique ID is stored everywhere in the system but its name is held in only one table. The name can be updated more easily in one row of one table. A typical update in such an example would be the RIM company changing its name to BlackBerry. That update would be done in one place and immediately the correct “BlackBerry” name would be displayed throughout the system.
In computing, denormalization is the process of attempting to optimize the read performance of a database by adding redundant data or by grouping data. In some cases, denormalization is a means of addressing performance or scalability in relational database software.
A normalized design will often store different but related pieces of information in separate logical tables (called relations). If these relations are stored physically as separate disk files, completing a database query that draws information from several relations (a join operation) can be slow. If many relations are joined, it may be prohibitively slow. There are two strategies for dealing with this. The preferred method is to keep the logical design normalized, but allow the database management system (DBMS) to store additional redundant information on disk to optimise query response. In this case it is the DBMS software’s responsibility to ensure that any redundant copies are kept consistent. This method is often implemented in SQL as indexed views (Microsoft SQL Server) or materialised views (Oracle, PostgreSQL). A view represents information in a format convenient for querying, and the index ensures that queries against the view are optimised.
The more common approach is to denormalize the logical data design. With care this can achieve a similar improvement in query response, but at a cost – it is now the database designer’s responsibility to ensure that the denormalized database does not become inconsistent. This is done by creating rules in the database called constraints, that specify how the redundant copies of information must be kept synchronised. It is the increase in logical complexity of the database design and the added complexity of the additional constraints that make this approach hazardous. Moreover, constraints introduce a trade-off, speeding up reads (SELECT in SQL) while slowing down writes (INSERT, UPDATE, and DELETE). This means a denormalized database under heavy write load may actually offer worse performance than its functionally equivalent normalized counterpart.
A denormalized data model is not the same as a data model that has not been normalized, and denormalization should only take place after a satisfactory level of normalization has taken place and that any required constraints and/or rules have been created to deal with the inherent anomalies in the design. For example, all the relations are in third normal form and any relations with join and multi-valued dependencies are handled appropriately.
About Tobias Riedner
Tobias Riedner foundet WYCDWD.com in 2015. He works and worked as innovator, consultant, analyst and educator in the fields of business intelligence and data warehousing. He learned a lot from the best consultants, managers und educators in the past and shares his knowledge worldwide. He works for a steady growing traditional company which is a leader in industry 4.0.