Best Data Model Schemas & Methods in 2024



As companies gather colossal amounts of data from various business operations, it becomes challenging to store and organize data in database servers for making data-driven decisions. In order to make effective business decisions, companies have to trace the common relationships that exist between their Customers and Products. There are various processes for identifying connections between data entities and attributes. One such approach is using Data Model Schemas, which organize the elements and attributes of data to map out their underlying connections and relationships. 

In this article, you will learn about Data Model Schemas and the best Data Model Schemas.

What are Data Model Schemas?

Data Model Schemas
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A Data Model Schema defines how data points are organized and connected within a Relational Database, including logical constraints like table names, fields, data types, and the relationships between these entities. In other words, a Data Model Schema is the “blueprint” of a database that describes how data can be related to other Tables or Data Models. Data Model Schemas are commonly used to communicate the architecture of a database using visual representations, and they serve as the foundation for an organization’s Data Management practice.

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Best Data Model Schemas & Methods

Choosing the right Data Model Schema can eliminate bottlenecks and anomalies during the execution of a software project. On the other hand, an improper Schema Design can cause several errors in an application and make refactoring expensive. For example, if you didn’t recognize early on that your application would require multiple tables JOINS, your service will eventually come to a halt when you hit a certain quantity of users and data. 

In order to resolve such complications, data will most likely need to be moved to new tables, code will need to point to those new tables, and the tables will require the appropriate JOINs. This implies you’ll need a very strong test environment (Database and Source Code) to test your changes, as well as a plan for managing Data Integrity and upgrading your database and source code at the same time. There is almost no turning back once you begin migrating your database to a new schema. To avoid such complexities in the early stages of a data project, it is critical that you select the suitable schema, thereby avoiding unprecedented bottlenecks.

The Data Model Schema design begins with a high level of abstraction and progresses like any design process to become more concrete and specific. Data Models are generally classified into three types based on their level of abstraction. The process will begin with a Conceptual Model, then move on to a Logical Model, and finally to a Physical Model. Such data models provide a conceptual framework for a database user to specify the requirements, structure, and set of attributes for configuring a Database Schema. A Data Model also provides users with a high-level design implementation that determines what can be included in the schema.

The best Data Model Schemas are given below.

1) Hierarchical Schema

Data Model Schemas: Hierarchical Schema
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The Hierarchical Schema resembles a family tree layout that represents one-to-many relationships of data. Data entities in this schema or model resemble “parents” or “children” and branch off from other data that has a relationship with them. In a tree-like format, Hierarchical Data Models represent one-to-many relationships. Each record in this model has a single root or parent table that maps to one or more child tables. 

In the Hierarchical Schema, a parent can have one or more children, but only one parent can be listed on a child record. The hierarchical schema also includes links, which are the connections between data types and records which specify the type of data contained in the field. Since the hierarchical schema organizes data into a tree-like structure with a single parent or root for each record, the records of siblings are also sorted in a specific order.

The Hierarchical Model was initially used in the IBM Information Management System (IMS) and quickly became popular, particularly in banking. This Hierarchical approach is still used in Extensible Markup Language (XML) systems and Geographic Information Systems (GISs).

2) Network Schema

Data Model Schemas: Network Schema
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The Network Schema is a hierarchical schema’s extension that allows many-to-many relationships between linked records, implying multiple parent records. The only distinction between this model and the hierarchical model is that a record can have multiple parents. The network technique is based on graph data structure, in which a data object is represented by a node, and the relationship between two nodes is represented by an edge.

The Network Model is built using sets of related records and is based on mathematical set theory. Each set includes one owner or parent record as well as one or more member or child records. Because a record can be a member or a child of multiple sets, this model can even comprehend complex relationships.

Furthermore, the network model allows users to represent objects and their relationships in a flexible manner. In the Network Schema, an object is represented within a node, and the relationship between nodes is represented as an edge, which allows each node to keep multiple parent and child records in a generalized manner.

3) Relational Schema

Data Model Schemas: Relational Schema
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The term “relational” refers to the various relationships that exist between the entities such as one to one, one to many, many to one, and many to many. This model is also similar to the Hierarchical Data Model in that it maps out the connections between various tables of data rather than parent-child relationships. With this Relationship Schema, users can explicitly join data segments through the use of tables, reducing database complexity.

In the Relational Schema, data is stored in tables and columns, where the relationships between the data elements are identified by the appropriate columns. The relational data modeling schemas do not necessitate a thorough understanding of the physical characteristics of the data storage system. Since relational models have structural independence features, users can change the structure of the database without altering the way to access data.

Relational Schemas often use Structured Query Language (SQL) for implementing Data Management Operations. In addition, relational models are good at preserving Data Integrity and reducing redundancy. They are commonly used in point-of-sale systems (POS) systems and other types of Transaction Processing.

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4) Object-Oriented Schema

Data Model Schemas: Object-Oriented Schema
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The Object-Oriented Data Model Schema incorporates the aspects of both object-oriented programming and relational data model. It encapsulates data and relationships in a single structure, along with attributes that define the object’s properties and methods that describe its behavior.

The object-oriented database models are divided into two types: Multimedia Model and Hypertext Model. A Multimedia Model includes media such as images that are impossible to store in a Relational Database, whereas a hypertext database allows any object to link to any other object in order to organize a large amount of disparate data. 

The Object-Oriented Data Model simplifies complex data points by grouping entities into class hierarchies. Both the data and the relationship are represented in this model by a single structure known as an Object. Users can store audio, video, images, and other types of data in the database, which was not possible in the Relational Model. 

5) Entity-Relationship Schema

Data Model Schemas: Entity-Relationship Schema
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The ER Schema (Entity-relationship Schema) is a high-level relational model used to define data elements and relationships for system entities, showing how data points connect with each other. This conceptual design provides a better representation of the data, making it easier for users to understand the overall relationship between data entities. The entire database can be represented with ER Model Schema by an entity-relationship diagram, which is made up of Entities, Attributes, and Relationships.

The entities component in the ER model represents people, places, things, events, or concepts for which data is processed and stored as tables, whereas the attributes component represents distinct characteristics or properties of an entity for which data is maintained and stored as data in columns. The Relationship component describes the relationship between two attributes by specifying logical relation between different entities that represent business rules or constraints.

The ER Schema is widely used in enterprise applications for relational databases, primarily for transaction processing. Since it has minimal redundancy and well-defined relationships, the ER schema is very efficient for data capture and update processes. 


In this article, you learned about Data Model Schemas and the best Data Model Schemas. This article covered only the five best Data Model Schemas that are used widely. However, there are also flat, semi-structured, dimensional, star, and graph Data Model Schemas that you can explore later. After exploring these Data Model Schemas, you can use data modeling tools to design your own database layout. In case you want to export data from a source of your choice into your desired Database/destination then Hevo Data is the right choice for you! 

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Freelance Technical Content Writer, Hevo Data

Ishwarya has experience working with B2B SaaS companies in the data industry and her passion for data science drives her to product informative content to aid individuals in comprehending the intricacies of data integration and analysis.

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