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Difference between Operational Database and Data Warehouse?
Operational Database
The Operational Database is the source of data for the data warehouse. It contains detailed data used to run the normal operations of the business. The data generally changes as updates are created and reflect the latest value of the final transactions. It is also called OLTP (Online Transactions Processing Databases), which are used to manage dynamic data in real-time.
The requirement of the operational database being simply controlled insertion and updating of information with efficient access to data manipulation and viewing mechanisms.
Data Warehouse
Data Warehouse Systems serve users or knowledge workers for data analysis and decision-making. Such systems can construct and present data in a specific structure to accommodate the diverse requirement of several users. These systems are known as Online-Analytical Processing (OLAP) Systems.
OLAP is a wide term that also surrounds data warehousing. In this model, data is saved in a format, which allows the effective creation of data mining/documents. OLAP design must accommodate reporting on very huge recordsets with little degradation in operational efficiency. The complete term that can define taking data structures in an OLTP structure and influencing the same data in an OLAP structure is “Dimensional Modeling” It is the basic building block of Data Warehousing.
Let us see the comparison between the Operational database and Data Warehouse.
| Operational Database | Data Warehouse |
|---|---|
| An OLTP system is customer-oriented and is used for transaction and query processing by clerks, clients, and information technology professionals. | An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives, and analysts. |
| An OLTP system handles current data that frequently are too detailed to be simply used for decision making. | An OLAP system handles large amounts of historical information, provides facilities for summarization and aggregation, and stores and manages data at multiple levels of granularity. These features create the data easier to use in informed decision-making. |
| An OLTP system generally adopts an entity-relationship (ER) data Model and an application-oriented database design. | An OLAP system typically adopts either a star or snowflake model and a subject-oriented database design. |
| An OLTP system focuses mainly on the current information inside an enterprise or department, without defining historical data or data in multiple organizations. | An OLAP system often spans multiple versions of a database schema, due to the evolutionary process of an organization. It can also deal with information that originates from different organizations, integrating information from many data stores. |
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