OLTP vs OLAP

OLTP vs OLAP

Diff b/w OLTP  and 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

And some more differences between OLTP and OLAP

                                   

application oriented	subject oriented
detailed	summarized, otherwise refined
accurate, as of the moment of access	represents values over time, snapshots
serves the clerical community	serves the managerial community
can be updated	is not updated
requirements for processing understood before initial development	requirements for processing not completely understood before development
compatible with the Software Development Life Cycle	completely different life cycle
performance sensitive	performance relaxed
accessed a unit at a time	accessed a set at a time
transaction driven	analysis driven
control of update a major concern in terms of ownership	control of update no issue
high availability	relaxed availability
managed in its entirety	managed by subsets
nonredundancy	redundancy
static structure; variable contents	flexible structure
small amount of data used in a process	large amount of data used in a process