Understanding Data Modeling: Concepts and Best Practices

What is Data Modeling?

Data Modeling is the process of analyzing data objects and their relationship to other objects. It is used to analyze the data requirements required for business processes. Data models are created for data storage in a database. The Data Model's main focus is on what data is needed and how to organize data rather than what operations we have to perform.

Data Model is basically an architect's building plan. It is a process of documenting complex software system design as in a diagram that can be easily understood. The diagram will be created using text and symbols to represent how the data will flow. It is also known as the blueprint for constructing new software or re-engineering any application. Here are some data modeling best practices:

1. Grain

2. Naming

3. Materialisation

4. Permission and Governance

Data Modeling in software engineering is the process of simplifying the diagram or data model of a software system by applying certain formal techniques. It involves expressing data and information through text and symbols.

What is Data Model? 

A data model provides a transparent picture of data to help create a real database. It shows you from data design to correct data implementation.

There are three types of Data Models 

Conceptual Model

The conceptual data model is a view of the data that is required to help business processes. It also keeps track of business events and keeps related performance measures

Logical Model

In the logical data model, the map of rules and data structures includes the required data, such as tables, columns, etc. Data architects and Business Analysts create the Logical Model

Physical Data Model

In a physical data model, the implementation is described using a specific database system. It defines all the components and services that are required to build a database. 

Data Modeling types

1. Hierarchical Model

The hierarchical model is a tree-like structure. There is one root node, or we can say one parent node and the other child nodes are sorted in a particular order. But, the hierarchical model is very rarely used now. This model can be used for real-world model relationships.

2. Object-oriented Model

The object-oriented approach creates objects containing stored values. The object-oriented model communicates while supporting data abstraction, inheritance, and encapsulation.

3. Network Model

The network model provides us with a flexible way of representing objects and relationships between these entities. It has a feature known as a schema representing the data in the form of a graph. An object is represented inside a node and the relation between them as an edge, enabling them to maintain multiple parent and child records in a generalized manner.

4. Entity-relationship Model

ER model (Entity-relationship model) is a high-level relational model which is used to define data elements and relationships for the entities in a system. This conceptual design provides a better view of the data that helps us easy to understand. In this model, the entire database is represented in a diagram called an entity-relationship diagram, consisting of Entities, Attributes, and Relationships.

5. Relational Model

Relational is used to describe the different relationships between the entities. And there are different sets of relations between the entities such as one to one, one to many.

What are the 6 Steps of Data Modeling?

Data modeling is the process of creating conceptual representations of data objects and their relationships to each other. The data modelling process usually consists of several steps, each steps of the process, data modelers work with stakeholders to understand data requirements, define entities and attributes, establish relationships between data objects, and create models that accurately represent the data in a way that applications can consume. . Developers, database administrators, and other interested parties. These workflows typically look like this:

1. Define an Entity

The data modeling process begins by identifying the entities, events, or concepts represented in the data set to be modeled. Each entity should be consistent and logically separated from others.

2. Define Key Properties for each Entity

Each type of object can be distinguished from all other objects because it has one or more unique properties, called attributes. For example, an entity called "Customer" might have attributes such as first name, last name, phone number, and job title, and an entity called "Address" might contain street name and number, city, state, country, and postal code.

3. Identify Relationships between Entities

An initial draft of the data model specifies the nature of each entity's relationship to other entities. In the example above, each customer "lives at the address." If this model is extended to include an entity called "Order", then each order will also be shipped and billed to that address. These relationships are usually documented using Unified Modeling Language (UML).

4. Mapping Properties to Entities

This allows the model to reflect how the business uses the data. Several formal data modeling patterns are widely used. Object-oriented developers often use analysis patterns or design patterns, while stakeholders in other business areas may refer to other patterns.

5. Reduce Redundancy in Performance Requirements

Normalization is a way to organize data models (and the databases they represent) by assigning numeric identifiers, called keys, to groups of data to represent relationships between models without repeating the data. For example, if each customer is assigned a key, that key  can be associated with both address and order history without having to repeat that information in a table of customer names. Normalization typically reduces the amount of disk space required by the database, but it can affect query performance.

6. Complete and Validate the Data Model

Data modeling is an iterative process that must be repeated and refined as business requirements change. 

What are the best Data Modeling tools?

The best tools are listed below:

1. ER/Studio

ER/Studio is Idera's powerful data modeling tool, enabling efficient classification of current data assets and sources across platforms. You can also create and share data models and track data provenance end-to-end. With ER/Studio, organizations can quickly understand the interactions between data, processes and people. Key Features of ER/Studio:

Let us know the use of google sheets as a database for HTML

2. DbSchema

DbSchema extends functionality to the JDBC driver and provides a complete GUI for sorting complex data. It provides a great user experience for SQL and NoSQL in general provides efficient reverse engineering. DbSchema serves as database users, administrators, and programmers, and is also considered a copy of the visualization of relationships between tables and other data models. 

Know more about difference between SQL and NoSQL.

3. HeidiSQL 

This free open source software is one of the most popular data modeling tools for MariaDB and MySQL worldwide. It also supports MS SQL, PostgreSQL and SQLite database systems. 

4. Toad Data Modeler

Toad Data Modeler is an ideal solution for cross-platform support for databases such as SAP, SQL Server, DB2, Microsoft Access, and more. Toad Data Modeler also provides developers with flexibility and offers easy customization and migration. This tool is also useful for building complex logical and physical objects (both forward and reverse engineering)

5. ERBuilder

ERBuilder Data Modeler allows developers to create graphs using interactive charts and can generate the most popular SQL data. It also provides a beautiful visual design environment, allowing developers to communicate information easily. 

A database that uses graph architecture for semantic inquiry with nodes, edges, and properties to represent and store data. Click to explore about our, Role of Graph Databases in Big Data Analytics

What are Data Modeling tools used for?

Data Modeling is a process of formulating data in an information system in a structured format. Below are certain practical uses of related tools in any sector or industry.

1. It helps create a robust design with a data model that can show an organization's entire data on the same platform.

2. The data model makes sure that all the data objects required by the database are represented or not.

3. The help data model can design the database at the logical, physical, and conceptual levels.

4. The relation tables, foreign keys, and primary keys can be defined with the data model's help.

5. The Tools help in the improvement of data quality.

6. Data Model gives the clear picture of business requirements.

7. Redundant data and missing data can be identified with the help of data models.

8. In data models, all the important data is accurately represented. The chances of incorrect results and faulty reports decreased as

the data model reduces data omission.

9. The data models create a visual representation of the data, improving the data analysis. We get the data picture, which developers can then use to create a physical database.

10. Better consistency can be qualified with the help of a data model across all the projects.

11. The model is quite time-consuming, but it makes maintenance cheaper and faster.

Conclusion of Data Modeling

In summary, Data Modeling helps visualize data. Models are built during the project's design and analysis phase to ensure that application requirements are fulfilled.

• Importance of Big Data Maturity Model Assessment.
• What do you understand by NoSQL Databases?