ETL -- Difference between OLTP & OLAP

 

Online Transaction Processing (OLTP)	Online Analytical Processing (OLAP)
Application Oriented	Used to analyze and forecast business needs
Up to date and consistent at all times	Data is consistent only up to the last update
Detailed data	Summarized data
Isolated data	Integrated data
Queries touch small amount of data	Queries touch large amounts of data
Fast response time	Slow response time
Updates are frequent	Updates are less frequent
Concurrency is the biggest performance concern	Each report or query requires lot of resources
Clerical Users	Managerial/Business Users
OLTP targets specific process like ordering from an online store	OLAP integrates data from different processes like (Ordering, processing, inventory, sales etc.,)
Performance sensitive	Performance relaxed
Few accessed records per time	Large volumes accessed at a time
Read/Update access	Mostly read and occasional update
No redundancy	Redundancy cannot be avoided
Databases size is usually around 100 MB to 100 GB	Databases size is usually around 100 GB to a few TB

 

OLTP vs. OLAP We can divide IT systems into transactional (OLTP) and analytical (OLAP). In general we can assume that OLTP systems provide source data to data warehouses, whereas OLAP systems help to analyze it. - 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. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF). - 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. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema). The following table summarizes the major differences between OLTP and OLAP system design. OLTP System Online Transaction Processing (Operational System) OLAP System Online Analytical Processing (Data Warehouse) Source of data 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 the data Reveals a snapshot of ongoing business processes Multi-dimensional views of various kinds of business activities 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 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