Online Transaction Processing
OLTP 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 from your account. Then Account Number, Withdrawal amount, Available Amount, Balance Amount, Transaction Number etc are operational data elementions.
Operational Data
Operational data are usually of local relevance
Frequent Updates
Normalized Tables
Point Query
In an OLTP system data are frequently updated and queried. So quick response to a request is highly expected. Since the OLTP systems invlove large number of update quiries, the database tables are optimized for write operations.
To prevent data redundancy and to prevent update anomalies the database tables are normalized.Set of tables that are normalized are fragmented.Normalization makes the write operation in the database tables more efficient.
Operational data are usually of local relevance.It involves Queries accessing individual tuple(individual record).These type of queries are termed as point queries.
Example :Bank account transaction
Online Analytical Processing (OLAP)
OLAP deals with Historical Data or Archival Data. Historical data are those data that are archived over a long period of time. Data from OLTP are collected over a period of time and store it in a very large database called Data warehouse. The Data warehouses are highly optimized for read (SELECT) operation.
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.
Analytical Query: Online Analytical Processing (OLAP) quries are of analytical form. Query need to access large amount of data and require huge number of aggregations. It access large number of records from database tables and perform the required operation based on this.
Historical Data or Archival Data
Infrequent updates
Analytical queries require huge number of aggregations
Integrated data set with a global relevance
Updates are very rare in a Data warehouse.OLAP quiries will give aggregated information about the things happend in the past over a period of time and this will help the management in strategic decision making. Hence OLAP Queries are of having significant importance in strategic decision making.
Example: profit changing over the years across different regions
OLTP 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 from your account. Then Account Number, Withdrawal amount, Available Amount, Balance Amount, Transaction Number etc are operational data elementions.
Operational Data
Operational data are usually of local relevance
Frequent Updates
Normalized Tables
Point Query
In an OLTP system data are frequently updated and queried. So quick response to a request is highly expected. Since the OLTP systems invlove large number of update quiries, the database tables are optimized for write operations.
To prevent data redundancy and to prevent update anomalies the database tables are normalized.Set of tables that are normalized are fragmented.Normalization makes the write operation in the database tables more efficient.
Operational data are usually of local relevance.It involves Queries accessing individual tuple(individual record).These type of queries are termed as point queries.
Example :Bank account transaction
Online Analytical Processing (OLAP)
OLAP deals with Historical Data or Archival Data. Historical data are those data that are archived over a long period of time. Data from OLTP are collected over a period of time and store it in a very large database called Data warehouse. The Data warehouses are highly optimized for read (SELECT) operation.
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.
Analytical Query: Online Analytical Processing (OLAP) quries are of analytical form. Query need to access large amount of data and require huge number of aggregations. It access large number of records from database tables and perform the required operation based on this.
Historical Data or Archival Data
Infrequent updates
Analytical queries require huge number of aggregations
Integrated data set with a global relevance
Updates are very rare in a Data warehouse.OLAP quiries will give aggregated information about the things happend in the past over a period of time and this will help the management in strategic decision making. Hence OLAP Queries are of having significant importance in strategic decision making.
Example: profit changing over the years across different regions
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