Databases intended for online transaction processing (OLTP) are
typically more normalized than databases intended for online
analytical processing (OLAP). OLTP applications are characterized by a
high volume of small transactions such as updating a sales record at a
supermarket checkout counter. The expectation is that each transaction
will leave the database in a consistent state. By contrast,databases
intended for OLAP operations are primarily “read mostly” databases.
OLAP applications tend to extract historical data that has accumulated
over a long period of time. For such databases,redundant or
“denormalized” data may facilitate business intelligence applications.
Specifically,dimensional tables in a star schema often contain
denormalized data. The denormalized or redundant data must be
carefully controlled during extract,transform,load (ETL) processing,
and users should not be permitted to see the data until it is in a
consistent state. The normalized alternative to the star schema is the
snowflake schema. In many cases,the need for denormalization has
waned as computers and RDBMS software have become more powerful,but
since data volumes have generally increased along with hardware and
software performance,OLAP databases often still use denormalized
schemas.

Denormalization is also used to improve performance on smaller computers as in computerized cash-registers and mobile devices,since these may use the data for look-up only (e.g. price lookups). Denormalization may also be used when no RDBMS exists for a platform (such as Palm),or no changes are to be made to the data and a swift response is crucial.