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.
- 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 decisionsupport
|
|
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 datarefreshes 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
|
|
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
|
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