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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