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.

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.

ER model contains normalized data where as Dimensional model contains denormalized data.

View:

- Tail raid data representation is provided by a view to access data from its table.

- It has logical structure can not occupy space.

- Changes get affected in corresponding tables.

Materialized view

- Pre calculated data persists in materialized view.

- It has physical data space occupation.

- Changes will not get affected in corresponding tables.

Star schema: A highly de-normalized technique. A star schema has one fact table and is associated with numerous dimensions table and depicts a star.

Snow flake schema: The normalized principles applied star schema is known as Snow flake schema. Every dimension table is associated with sub dimension table.

Differences:

- A dimension table will not have parent table in star schema, whereas snow flake schemas have one or more parent tables.

- The dimensional table itself consists of hierarchies of dimensions in star schema, where as hierarchies are split into different tables in snow flake schema. The drilling down data from top most hierarchies to the lowermost hierarchies can be done.

Dimensional modeling is often used in Data warehousing. In simpler words it is a rational or consistent design technique used to build a data warehouse. DM uses facts and dimensions of a warehouse for its design. A snow and star flake schema represent data modeling.

OLTP stands for OnLine Transaction Processing. Applications that supports and manges transactions which involve high volumes of data are supported by OLTP system. OLTP is based on client-server architecture and supports transactions across networks.

OLAP stands for OnLine Analytical Processing. Business data analysis and complex calculations on low volumes of data are performed by OLAP. An insight of data coming from various resources can be gained by a user with the support of OLAP.

Business facts or measures and foreign keys are persisted in fact tables which are referred as candidate keys in dimension tables. Additive values are usually provided by the fact tables which acts as independent variables by which dimensional attributes are analyzed.

Attributes that are used to constrain and group data for performing data warehousing queries are persisted in the dimension tables.

Data mining is a method for comparing large amounts of data for the purpose of finding patterns. Data mining is normally used for models and forecasting. Data mining is the process of correlations, patterns by shifting through large data repositories using pattern recognition techniques.

Data warehousing is the central repository for the data of several business systems in an enterprise. Data from various resources extracted and organized in the data warehouse selectively for analysis and accessibility.

Data warehousing is a process of repository of electronic data of an organization. For the purpose of reporting and analysis, data warehousing is used. The essence concept of data warehousing is to provide data flow of architectural model from operational system to decision support environments.