A data model is a set of business rules and data structures. It turns data into a visual representation and shows the relationships between various data items. It also answers the data elements' who, what, where, and why. For example, in a retail transaction, the data model offers information on the buyer, the item(s) purchased, and the time of purchase. Here we are going to discuss the data models that you will need to know.

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A data model is a set of business rules and data structures. It turns data into a visual
representation and shows the relationships between various data items. It also answers the data
elements' who, what, where, and why. For example, in a retail transaction, the data model offers
information on the buyer, the item(s) purchased, and the time of purchase. Here we are going to
discuss the data models that you will need to know.
The model may also include additional information on the customer, product, store, salesperson,
manufacturer, supply chain, and much more. A Big Data Model gives a brief overview of the
evolution of modeling to gain a deeper understanding of the standard data modeling strategies
used today. Some data models are currently the most frequently used.
Entity-Relationship Data Model
This paradigm has been widely adopted for relational databases used in enterprise applications,
especially for transaction processing. It is highly effective for data gathering and updating
operations, requiring little redundancy and having well-defined relationships. By the following
elements, the model is made:
Entities that stand in for individuals, groups, locations, objects, occasions, or ideas; characteristics
are unique properties.

2. Attributes of an entity that are kept up to date and saved as columns of data; and relationships,
which specify logical connections between two objects that stand in for limitations or business
rules.
Dimensional Data Model
The dimensional model has relationships and properties, just like the entity-relationship model.
However, it has two main parts.
Facts are measurements of an event or activity, whether a business transaction or a person or
device-related occurrence. Fact tables are normalized and largely redundant, with most facts
being numerical.
Dimensions are tables that specify the who, what, where, and why characteristics of the facts in
the context of business. Usually, dimensions are not numerical but rather descriptive.
Graph Data Model
The network modeling method from the Data Warehouse Data Model is the source of graph data
modeling. Primarily used for intricate relationship modeling in graph databases, it can also be
applied to other NoSQL databases, including document and key-value databases.
Two essential components make up a graph data model.
Nodes are entities that possess a distinct identity. In the relational model, every instance of an
entity is a particular node, similar to a row in a table.
Edges can also refer to relationships or links. They establish connections between nodes and their
relationships. There must be a minimum of one edge connecting each node. Edges can be
directed, meaning the interaction between nodes goes in a specific direction, or undirected,
meaning there is a bidirectional relationship between nodes.
Due to its application in various business processes and analytics applications, data frequently
has many contexts. As a result, a company requires multiple Meta Model rather than just one
that governs all applications.
Conclusion
A data model, sometimes a Metadata Model, plays a vital role for a company. Effective operational
systems, business intelligence, and analytics applications that generate value for the company by
converting corporate data into valuable information assets are all built around well-designed data
models.