What is the difference between Data modelling and Dimensional modelling?

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ThomasB

  • Jan 4th, 2008
 

Dimensional modeling is modeling of information as logically appropriate for the business segments.

Data modeling is the modeling of information as appropriate for physical data storage.

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tirukosri

  • Mar 12th, 2008
 

Dimensional Modelling is the Analysis of the Transactional Data (Facts) based on Master Data (Dimensions).

Data Modeling is the process of creating a data model by applying a data model theory to create a data model instance.

Regards,
Sridhar Tirukovela

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Blueash

  • Nov 15th, 2008
 

Modelling data storage optimized for Transactional Processing is Data Modelling.
Modelling data storage optimized for Analytical Processing is Dimension Modelling.

mri_sonam

  • Dec 17th, 2008
 

When data modeling, we are structuring and organizing data. These data structures are then typically implemented in a database management system. In addition to defining and organizing the data, data modeling will impose (implicitly or explicitly) constraints or limitations on the data placed within the structure.

Managing large quantities of structured and unstructured data is a primary function of information systems. Data models describe structured data for storage in data management systems such as relational databases.

Data warehouses are typically developed using dimensional models rather than the traditional entity/relationship models associated with conventional relational databases.

Dimensional Modelling is a design concept used by many data warehouse
designers to build their datawarehouse. In this design model all the data is stored in two types of tables - Facts table and Dimension table. Fact table contains the facts/measurements of the business and the dimension table contains the context of
measurements ie, the dimensions on which the facts are calculated.

Data is
modeled as a hypercube, and the schema is a so-called star schema with a centralised fact table surrounded by smaller dimensional tables representing key scientific objects.

Dimensional database systems allow multidimensional data to be
modeled natively. Or they can be modeled using the star schema or snowflake schema.

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