The relational database model defines a method of structuring data into related tables. Each entity is represented by a table. Each column represents an attribute of the entity. Each instance of an entity is represented by a row. This structure makes it easy to update and retrieve information. However, relational databases are not appropriate for storing all types of data or for every data retrieval situation. The relational database model has significant limitations for some data storage and retrieval scenarios.
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As the amount of data processed by an application grows, the increasing volume puts a strain on the relational database infrastructure. Normalisation can help reduce the amount of redundant data stored, but highly normalised data requires many joins during data retrieval, so data is often denormalized to enhance data retrieval performance.
Poor Support for Unstructured Data
The relational database model requires that a structure be imposed on data. Some data is unstructured and does not map well to a set of related tables.
Not Suited to Analytics
The relational model is flat, or two-dimensional. Some analytic scenarios require multidimensional structures, such as online analytical processing (OLAP) cubes.
Efficient relational database storage requires ongoing maintenance. Indexes must be created and maintained as requirements change. Sometimes data must be restructured to meet changing business requirements.
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