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