OLTP Vs OLAP 博客分类： Common OLTPOLAP

 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) System deals with operational data. Operational data are those data  involved in the operation of a particular system.

 

Example: In a banking System, you withdraw amount through an ATM. Then account Number,ATM PIN Number,Amount  you are withdrawing, Balance amount in account etc are operational data elements.

 

OLAP (On-line Analytical Processing) System deals with Historical Data or Archival Data. Historical data are those data that are archived over a long period of time.

 

OLAP is also referred to as DSS (Decision Support System).

 

Example: If we collect last 10 years data about flight reservation, The data can give us many meaningful information such as the trends in reservation. This may give useful information like peak time of travel, what kinds of people are traveling in various classes (Economy/Business)etc.

 

The biggest difference between an OLTP and an OLAP system is the amount of data analyzed in a single transaction. Whereas an OLTP handles many concurrent users and queries touching only a single record or limited groups of records at a time, an OLAP system must have the capability to operate on millions of records to answer a single query.The following table summarizes the differences between OLPT and OLAP:

 

CHARACTERISTIC of OLTP
System scope/view            Single business process (Operational: ERP, CRM)
Data sources                     One
Data model                        Static
Dominant query type           Insert/update
Data volume per transaction Small
Data volume                       Small/medium
Referesh                            Immediate
Bulk load/insert/update       No
History data                       Not available
Response times                < 1 second
System availability            High “24/7″
Typical user                      Front office
Number of users               Large
Example:
What is the Salary of Mr.Shyam?
What is the address and email id of the person who is the head of science department?

CHARACTERISTIC of OLAP
System scope/view          Multiple business subjects (Decision Support System)
Data sources                   Many
Data model                      Dynamic
Dominant query type        Read
Data volume per transaction    Big
Data volume                    Large
Referesh                        Periodic
Bulk load/insert/update    Yes
History data                   Available
Response times             Can be in minutes
System availability          Relaxed “8/5″
Typical user                    Managers/ Executive
Number of users              Small/medium

 

 

 

 

 

 

 

- 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