Relational Databases VS Graph Databases

Relational databases and graph databases represent two major approaches for modeling and querying data. Here we’ll discuss difference between relational database vs graph databases.

Some common differences are based on these criteria. Let’s start with introduction

Introduction

Relational databases have been the predominant data management technology for decades. The relational model organizes data into tables with rows and columns, with defined relationships between the tables.

Characteristics of relational databases are a predefined schema, use of SQL for querying, and adherence to ACID principles for reliability. The rigid structure of relational databases makes them well suited for applications requiring complex transactions and analytics. However, they can be restrictive when business requirements change rapidly.

Graph databases is being used in recent years as an alternative database solution optimized for storing and querying interconnected data. Graph databases structure data as nodes and edges in a large network. They allow flexible schema design and use graph algorithms to support complex traversals through node relationships.

By modeling entities and relationships directly, graph databases are highly performant for connected data analysis and provide a powerful way to visualize and explore connections in data. Their flexible schema design supports rapid evolution of data requirements.

Relational Databases VS Graph Databases

Here is a comparison table summarizing the key differences between relational databases and graph databases:

Parameter	Relational Databases	Graph Databases
Data Model	Tabular with rows and columns	Graph networks with nodes and edges
Schema	Predefined schema	Flexible, schema-on-read
Relationships	Foreign keys	Nodes linked by edges
Query Language	SQL	Traversal APIs and languages
Strengths	Structured data, transactions	Interconnected data, relationships
Use Cases	OLTP, business applications	Social, recommendations, fraud detection
Examples	MySQL, PostgreSQL, SQL Server	Neo4j, TigerGraph, Amazon Neptune
Market Value	The global relational database management system (RDBMS) market is expected to grow from $68.55 billion in 2022 to $133.45 billion by 2028.	The global graph database market is expected to grow from $2.2 billion in 2022 to $11.4 billion by 2028.
Well-known Companies	Relational databases are used by a wide range of companies including Amazon, Apple, Bank of America, eBay, Facebook, Google, IBM and Microsoft	Graph databases are being used by a wide range of companies, including Google, Amazon, Facebook, and Microsoft.
Industry Usage	Relational databases are used in a variety of industries, including banking, retail, healthcare, and manufacturing.	Graph databases are used in a variety of industries, including healthcare, financial services, and retail.

Key Differences

These are some detailed points which will help you both relational databases and graph databases.

Data Models

• Relational databases organize data into relations (tables) with defined schemas and enforce integrity constraints. Foreign keys establish connections between tables.
• Graph databases store data in nodes and edges. Nodes represent entities while edges represent relationships between entities. Both nodes and edges can have properties.

Data Structures

• Relational databases store data in tabular form – rows and columns. This can be inefficient for highly interconnected data.
• Graph databases use network structures with nodes connected by edges. This allows efficient representation of relationships.

Schemas

• Relational databases require predefined schema for tables and strict data types. Changing schemas can be disruptive.
• Graph databases are schema flexible. Nodes and edges can contain arbitrary data without enforcing types. Easy to evolve schemas.

Joins

• Relational databases use join operations to connect data across tables. Joins can get expensive for complex relationships.
• Graph databases directly associate entities via explicit edges. Traversals along edges replace joins.

Query Languages

• Relational databases support declarative SQL queries for accessing and manipulating data.
• Graph databases use traversals expressed through pattern matching and path finding APIs and query languages.

Use Cases

• Relational databases optimized for structured business data and transactions. Widely used across industries.
• Graph databases ideal for interconnected data and network-based domains like social, fraud detection, recommendations.

Examples

• Relational databases: MySQL, Oracle, PostgreSQL, Microsoft SQL Server
• Graph databases: Neo4j, Amazon Neptune, TigerGraph, JanusGraph

Which type of database is right for you?

The best choice of database for a particular application depends on the specific requirements of the organization. If you need to store and query structured data, and you need to perform complex transactions and analytics, then a relational database is a good choice. If you need to store and query interconnected data, and you need to perform complex network analysis, then a graph database is a good choice.

Here are some examples of when to use each type of database:

• Relational databases:
  • Customer relationship management (CRM) systems
  • Enterprise resource planning (ERP) systems
  • Financial management systems
  • E-commerce platforms
• Graph databases:
  • Social network analysis
  • Fraud detection
  • Recommendation systems
  • Geospatial data analysis
  • Sensor data analysis