If OLTP databases were used to
create such aggregates on the fly, the database resources used would impact the
ability to process transactions in a timely manner. These ad hoc queries often
leverage compute-intensive analytic functions embedded in the database.
A significant portion of the data in a data warehouse is often read-only, with infrequent
updates
Leveraging database manageability features can make it possible to deploy warehouses
holding hundreds of terabytes of data, even where near real-time updates
of some of the data is occurring.
The data in OLTP systems is not ???clean??? or consistent across systems
Data input to OLTP systems, if not carefully controlled, is likely to contain
errors and duplication. Often, a key portion of the data warehouse loading process
involves elimination of these errors. In addition, since multiple OLTP
systems might differ in common data definitions, the loading process can be
used to consolidate this data into a single definition.
The design required for an efficient data warehouse differs from the standard normalized
design for a relational database
Queries are typically submitted against a fact table, which may contain summarized
data.
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