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. 

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- 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).  For example, a bank storing years of historical records of check deposits could use an OLAP database to provide reporting to business users. 


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
source: www.rainmakerworks.com

Data Warehouse Info

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