Dimensional Modeling (DM) is a favorite modeling technique in data warehousing. In DM, a
model of tables and relations is constituted with the purpose of optimizing decision support
query performance in relational databases, relative to a measurement or set of measurements of
the outcome(s) of the business process being modeled. In contrast, conventional E-R models
are constituted to (a) remove redundancy in the data model, (b) facilitate retrieval of individual
records having certain critical identifiers, and (c) therefore, optimize On-line Transaction
Processing (OLTP) performance.
The strenghts of Dimensional Model as compared to E-R model are as follows:
1. Dimensional modelling is very flexible for the user perspective. Dimensional data model is mapped for creating schemas. Where as ER Model is not mapped for creating shemas and does not use in conversion of normalization of data into denormalized form.
2. ER Model is utilized for OLTP databases that uses any of the 1st or 2nd or 3rd normal forms, where as dimensional data model is used for data warehousing and uses 3rd normal form.
3. ER model contains normalized data where as Dimensional model contains denormalized data.
4. ER modeling that models an ER diagram represents the entire businesses or applications processes. This diagram can be segregated into multiple Dimensional models. This is to say, an ER model will have both logical and physical model. The Dimensional model will only have physical model.
5. E-R modelling revovles around the Entities and their relationships to capture the overall process of the system. where as Dimensional model/Muti-Dimensinal Modelling revolves around Dimensions(point of analysis) for decison making and not to capture the process.
model of tables and relations is constituted with the purpose of optimizing decision support
query performance in relational databases, relative to a measurement or set of measurements of
the outcome(s) of the business process being modeled. In contrast, conventional E-R models
are constituted to (a) remove redundancy in the data model, (b) facilitate retrieval of individual
records having certain critical identifiers, and (c) therefore, optimize On-line Transaction
Processing (OLTP) performance.
The strenghts of Dimensional Model as compared to E-R model are as follows:
1. Dimensional modelling is very flexible for the user perspective. Dimensional data model is mapped for creating schemas. Where as ER Model is not mapped for creating shemas and does not use in conversion of normalization of data into denormalized form.
2. ER Model is utilized for OLTP databases that uses any of the 1st or 2nd or 3rd normal forms, where as dimensional data model is used for data warehousing and uses 3rd normal form.
3. ER model contains normalized data where as Dimensional model contains denormalized data.
4. ER modeling that models an ER diagram represents the entire businesses or applications processes. This diagram can be segregated into multiple Dimensional models. This is to say, an ER model will have both logical and physical model. The Dimensional model will only have physical model.
5. E-R modelling revovles around the Entities and their relationships to capture the overall process of the system. where as Dimensional model/Muti-Dimensinal Modelling revolves around Dimensions(point of analysis) for decison making and not to capture the process.
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