Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
There are three different types of data models produced while progressing from requirements to the actual database to be used for the information system. They are,
- Conceptual data model
- Logical data model
- Physical data model
Process Diagram
The data modeling process. The figure illustrates the way data models are developed and used today.
A conceptual data model is developed based on the data requirements for the application that is being developed, perhaps in the context of an activity model.
The data model will normally consist of entity types, attributes, relationships, integrity rules, and the definitions of those objects.
This is then used as the start point for interface or database design.
The process of designing a database involves producing the previously described three types of schemas – conceptual, logical, and physical.
A fully attributed data model contains detailed attributes (descriptions) for every entity within it. The term “database design” can describe many different parts of the design of an overall database system.
An efficiently designed basic data model can minimize rework with minimal modifications for the purposes of different systems within the organization.
Top 5 Best Tools For Data Modeling
Db Schema helps to design and document databases, share the design model in a team, deploy the schema on multiple databases.
It supports all relational and No-SQL databases, including MySQL, PostgreSQL, SQLite, Microsoft SQL Server, MongoDB, MariaDB, Redshift, Snowflake, Google, and more.
The tool enables you to design & interact with the database schema, create comprehensive documentation and reports, work offline, synchronize the schema with the database, and so much more.
ER/Studio is compatible with multiple database platforms and is used by data architects, data modelers, database administrators, and business analysts to create and manage database designs, document, and reuse data assets.
Users can utilize ER/Studio to take conceptual data models and create a logical data model that is not dependent on specific database technology.
The software includes features to graphically modify the model, including dialog boxes for specifying the number of entities–relationships, database constraints, indexes, and data uniqueness.
Oracle SQL Developer Data Modeler is a free graphical tool that enhances productivity and simplifies data modeling tasks.
Using Oracle SQL Developer Data Modeler users can create, browse, and edit, logical, relational, physical, multi-dimensional, and data type models.
The Data Modeler provides forward and reverse engineering capabilities and supports collaborative development through integrated source code control.
The Data Modeler can be used in both traditional and cloud environments.
IBM InfoSphere Data Architect is a data modeling solution that simplifies and accelerates data integration design for business intelligence and statistics.
It is one of the best data modeling software that helps to align services, applications, data architectures, and processes.
A data design solution that enables you to discover, model, relate, standardize, and integrate diverse and distributed data assets throughout the enterprise.
Small teams or large distributed teams can use InfoSphere Data Architect as a plug-in to a shared Eclipse instance or share artifacts through standard configuration management repositories.
Erwin Data Modeler (Erwin DM) is an award-winning data modeling tool used to find, visualize, design, deploy and standardize high-quality enterprise data assets.
It reduces complexity, making it easier to design, deploy and understand data sources to meet business needs.
This tool automates and standardizes model design tasks, including complex queries, to improve business alignment, ensure data integrity and simplify integration.
Automatically generate data models and database designs to increase efficiency and reduce errors.
