Compare OLTP and Data Warehousing Environments

Following figure shows the key differences between OLAP and DataWarehousing Environments




One major difference between the types of system is that data warehouses are not
usually in third normal form (3NF), a type of data normalization common in OLTP
environments.

Data warehouses and OLTP systems have very different requirements. Here are some
examples of differences between typical data warehouses and OLTP systems:
■ Workload
Data warehouses are designed to accommodate ad hoc queries. You might not
know the workload of your data warehouse in advance, so a data warehouse
should be optimized to perform well for a wide variety of possible query
operations.
OLTP systems support only predefined operations. Your applications might be
specifically tuned or designed to support only these operations.
■ Data modifications
A data warehouse is updated on a regular basis by the ETL process (run nightly or
weekly) using bulk data modification techniques. The end users of a data
warehouse do not directly update the data warehouse.
In OLTP systems, end users routinely issue individual data modification
statements to the database. The OLTP database is always up to date, and reflects
the current state of each business transaction.
■ Schema design
Data warehouses often use denormalized or partially denormalized schemas (such
as a star schema) to optimize query performance.
OLTP systems often use fully normalized schemas to optimize
update/insert/delete performance, and to guarantee data consistency.
■ Typical operations
A typical data warehouse query scans thousands or millions of rows. For example,
"Find the total sales for all customers last month."
A typical OLTP operation accesses only a handful of records. For example,
"Retrieve the current order for this customer."
■ Historical data
Data warehouses usually store many months or years of data. This is to support
historical analysis.
OLTP systems usually store data from only a few weeks or months. The OLTP
system stores only historical data as needed to successfully meet the requirements
of the current transaction.

Posted in: Data Warehouse