Online 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).
Requirements
OLTP is a methodology to provide end users with access to large amounts of
data in an intuitive and rapid manner to assist with deductions based on
investigative reasoning.
Online transaction processing increasingly requires support for transactions
that span a network and may include more than one company. For this reason, new
online transaction processing software uses client or server processing and
brokering software that allows transactions to run on different computer
platforms in a network.
In large applications, efficient OLTP may depend on sophisticated
transaction management software (such as CICS) and/or database
optimization tactics to facilitate the processing of large numbers of
concurrent updates to an OLTP-oriented database.
For even more demanding Decentralized database systems, OLTP brokering
programs can distribute transaction processing among multiple computers
on a network. OLTP is often integrated into service-oriented architecture (SOA) and Web services.
Benefits
Online Transaction Processing has two
key benefits: simplicity and efficiency. Reduced paper trails and the faster,
more accurate forecasts for revenues and expenses are both examples of how OLTP
makes things simpler for businesses.
Difference between OLTP and OLAP
Online Transaction Processing (OLTP)
Online Analytical processing (OLAP)
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
Online Transaction Processing (OLTP)
Online Analytical processing (OLAP)
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
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