People are still confused when it comes to attributing what constitutes an OLTP or an OLAP system. The basic differentiation is based on the type of use and the function that the system will perform. The underlying technology or platform can be the same for both OLTP and OLAP systems, but the true differentiating factor is the logical function of the system itself.
If the system is continuously supporting the day-to-day requirements of the business (ATM’s, Checkout Systems, Order Processing, e-Commerce are all examples of OLTP systems.
OLAP systems are primarily the domain of business users who want to analyse the dynamics of the business in unique and innovative ways supported by the rich base of data that the organisation has accumulated through its OLTP systems. This can be analysis of customer usage patterns of the services offered by the company or it can be sales patterns of products based on marketing campaigns.
OLAP usage results in fewer transactions (database queries/transactions) but process huge volumes of data. OLTP systems typically process millions of transactions but process a microscopic amount of data.
To describe the two concepts (summary version!):
– OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second.
– OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques.
|OLTP Systems||OLAP Systems|
|Data Source||Operational data; OLTPs are the original source of the data.||Consolidation data; OLAP data comes from the various OLTP Databases|
|Purpose of data||To control and run fundamental business tasks||To help with planning, problem solving, and decision support|
|What is the data||Reveals a snapshot of ongoing business processes||Multi-dimensional views of various kinds of business activities that supports historical data|
|Inserts and Updates||Short and fast inserts and updates initiated by end users||Periodic long-running batch jobs refresh the data|
|Queries||Relatively standardized and simple queries returning relatively few records||Often complex queries involving aggregations|
|Processing Speed||Typically very fast||Depends on the amount of data involved; batch data refreshes and complex queries may take many hours; query speed can be improved by creating indexes or predefined summary data tables|
|Space Requirements||Can be relatively small if historical data is archived||Larger due to the existence of aggregation structures and history data; requires more indexes than OLTP|
|Database Design||Highly normalized with many tables||Typically de-normalized with fewer tables; use of star and/or snowflake schemas|
|Backup and Recovery||Backup religiously; operational data is critical to run the business, data loss is likely to entail significant monetary loss and legal liability||Instead of regular backups, some environments may consider simply reloading the OLTP data as a recovery method|