Data Modeling is a way to create a conceptual representation of data
structures ( tables ) that should exist in a database or targeted to be
placed into a database. The is a significant and powerful way to
visualize and communicate the business requirements. The data
model visually represents the soul of the data, business rules that
govern the data and how it is connect together in the database. A data
model can be comprised of two different designs and they are Logical and
Physical. The data model helps the functional and technical
teams in designing the database. The data model also allows the teams
to create validation tests to make sure that business rules are
enforced. The concept of data modeling can be better understood
if we compare the
development cycle of a database to the construction of a house. For
example
a company is planning to build a wood cabin (database) and the company
calls a building architect (data modeler) and gathers it's building
requirements
(business requirements). The building architect (data modeler) develops
the plan
(data model) and gives it to the company. Finally the company calls in
engineers (DBA) to construct the cabin(database). Reason(s) for a
developing a Data Model:- A new application for storing data
is needed.
- OLTP - Online Transaction Processing
- ODS -
Operational Data Store
- Data Warehouse
- Data Mart
- Rewriting
a data model from an existing system that may need to change for
- Business
requirements
- New data items
- Performance Improvements
- Incorrect
existing data model
- Database has no data model
Different Types of Database Model(s) | Model Type | Description | | Hierarchical | This data model employs a tree-like structure in which the data relationship is analogous to a parent-child relationship. In this model, a parent can have more than one child, but a child can not have more than one parent. Data relationships in this model are assimilated into various levels in while files are arranged in layers or tiers, and data is accessed through predefined relationships. The model enhances efficiency, provides greater control over a database, and simultaneously reduces redundancy in the database. | | Network | This data model is based on the connectivity of data relationships, where multiple computers are used and information is stored and shared. It provides two alternative views of a database - schema and subschema. Schema is a complete logical view of the database, and subschema is a subordinate view of the database. Further, data relationships result in reduced redundancy. | | Relational | The data model stores logically related data consistently in the form of related tables. These tables are linked through common fields or columns. The data stored is independent of files and is managed by a central database engine that processes queries and manipulates data. Every row of data contains an identification key that identifies data uniquely and helps in reducing redundancy. |
Advantages
and Significance of a Data Model- Make sure that all
requirements supplied by the functional team are completed and
accurately exist.
- The model is detailed enough to be used by the
technical team so that they can build the physical database
- Can
be used to communicate business rules within the organization
- Information
contained in the model will be used to design business rules,
relational tables, referential integrity (primary [PK] and foreign [FK]
keys), stored procedures, and triggers
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