When storing data, developers and database administrators are more concerned about how the data is stored. They are least bothered about how the data will be read. The chosen data storage format is usually closely related to the format of the data, requirements for managing data size and data integrity, and the kind of store in use. For example, when using a NoSQL document store, the data is often represented as a series of aggregates, each containing all of the information for that entity. However, this can have a negative effect on queries. When a query only needs a subset of the data from some entities, such as a summary of orders for several customers without all of the order details, it must extract all of the data for the relevant entities in order to obtain the required details.

To support efficient querying, a common solution is to generate, in advance, a view that materializes the data in a format suited to the required results set. The materialized view pattern describes generating prepopulated views of data in environments where the source data isn't in a suitable format for querying, where generating a suitable query is difficult, or where query performance is poor due to the nature of the data or the data store.

These materialized views, which only contain data required by a query, allow applications to quickly obtain the information they need. In addition to joining tables or combining data entities, materialized views can include the current values of calculated columns or data items, the results of combining values or executing transformations on the data items, and values specified as part of the query. A materialized view can even be optimized for just a single query. This pattern can help support efficient querying and data extraction, and improve application performance.