Comparing Graph Databases with Relational Databases

In this article we are going to discuss about graph databases and relational databases deeply compare and contrast between graph databases and relational databases to also highlighting some of the use case where each excels.

What is a graph database ?
This is defined as a specialized single-purpose platform for creating and manipulating graphs. Graphs contain nodes, edges, and properties, all of which are used to represent and store data in a way that relational databases are not equipped to do.

What is a relational databases?
This is a type of database that stores and provides access to data points that are related to one another. Relational databases are based on the relational model, an intuitive, straightforward way of representing data in tables. In a relational database, each row in the table is a record with a unique ID called the key. The columns of the table hold attributes of the data, and each record usually has a value for each attribute, making it easy to establish the relationships among data points.

To be able to compare and contrast between graph databases and relational database we will have to review the following factors to clearly understand graph databases and relational databases.The following are factors that will help you differentiate between graph databases and relational databases:

1. - Schema Flexibility
2. - Query Language
3. - Data Structures
4. - Performance
5. - Scalability
6. - Complexity

Schema Flexibility
Graph databases are usually schema-less or have flexible schema, which means you can add new types of relationships or properties to nodes without modifying the entire database schema.while Relational databases require a predefined schema, which can be limiting when dealing with evolving or semi-structured data.

Query Language
Graph databases use graph query languages like Cypher, which are designed to express relationships more effectively.while relational databases are commonly use SQL for querying

Data structure
Graph databases are designed around graph theory, representing data as nodes (entities) and edges (relationships). This allows for efficient representation of complex relationships.In contrast Relational databases organize data into structured tables with rows and columns, making them ideal for well-defined and structured data.

Scalability
Graph databases scale well for highly connected data because they are optimized for traversing relationships.while relational databases might struggle to scale efficiently for certain use cases.

Performance
Graph databases outperform relational databases in scenarios where the relationships between data points are a primary focus, such as social networks, recommendation engines, and fraud detection while Relational databases are efficient for simple queries involving structured data, making them suitable for transactional and reporting systems.

Scenarios where graph database and relational databases work

Graph Databases can be used when:

• The data has complex relationships.
• Traversing relationships efficiently is a primary concern.
• The schema may evolve over time.

Relational Databases can be used when:

• The data is highly structured and fits well into tables.
• Complex queries with multiple joins are common.
• ACID compliance and data integrity are critical.
• The schema is well-defined and stable

Lastly we are going to define some of the practical use case of graph databases and relational databases

Graph Databases

Fraud Detection
Graph databases are instrumental in fraud detection by helping uncover irregular and suspicious connections within data. The detection of fraudulent activities often relies on identifying unusual patterns and relationships that might go unnoticed with conventional database systems. Graph databases are well-equipped to navigate complex networks of data, making them indispensable for preempting fraudulent behavior.

Knowledge Graphs
Building knowledge graphs is a specialized use case where graph databases thrive. In the quest to organize vast repositories of information, graph databases shine by enabling semantic searches and exploration of interconnected knowledge. The relationships between various pieces of information are preserved, offering a comprehensive understanding that conventional databases struggle to provide.

Real-time Decision Support
In scenarios demanding real-time, data-driven decisions, graph databases offer a critical advantage. They swiftly reveal the relationships and connections within data, allowing organizations to make informed decisions on the spot.

Relational Databases

ACID Compliance:
If your application demands strong ACID (Atomicity, Consistency, Isolation, Durability) compliance, which ensures data integrity and reliability, a relational database is the preferred choice. It’s especially vital for applications like financial systems and healthcare records.

Structured Data with Well-Defined Schemas:
Financial Systems: Storing transactional data with clear schemas (e.g., accounts, transactions).
Inventory Management: Tracking products, quantities, and orders.

Data Integrity and Constraints:
Enforcing Rules: Ensuring data adheres to defined constraints (e.g., foreign key relationships, unique constraints).

Conclusion
When choosing which database to use for your database need one has to consider all the factors within so as to select the optimal database for efficient data storage, retrieval, and management.