Data remains an invaluable resource. When processed properly, it fuels savvy business decisions. How you store data has a lot to do with the ways you’re able to further process it.
Databases come in handy when you need to hold onto information that will help you make better choices. Different types of databases, such as graph databases, vector databases, and relational databases, have specific purposes and use cases.
In this article, we’ll help you learn about graph database vs relational database, including how they differ from one another.
A graph database is a NoSQL database that stores information as a network graph. It has four components.
Related: SQL vs. NoSQL databases: which one is right for you?
Graph databases handle data with complicated relationships more adeptly than relational, making them ideal for a wide range of applications.
The flexible format of a graph databases representation allows it to perform better than a relational database. It uses index-free adjacency, which means each network node contains pointers to related nodes.
As a result, the node provides access to extensive indices so performance improves depending on the number of traversal relationships.
Graph databases capably generate and query data with complex relationships and show value in the uses cases below.
In case of credit card fraud, graph databases highlight shared identifiers like social security numbers or addresses associated with the affected card. Known as link analysis, this technique works by evaluating nodes and edges within a network.
The graph database works great for applications that provide suggestions. It’s possible to save graph links between data types like customer interests, friends, and purchase history.
You can set up a widely available graph database to recommend products to users based on which products have been purchased by people with similar interests and buying histories.
These recommendation capabilities make graph databases useful in e-commerce and social networking platforms.
Route optimization examines a dataset and determines the values that best suit a certain scenario. For instance, use a graph database to:
Graph databases identify complicated interrelationships and hidden patterns in data. To give an example, a social media platform might employ graph databases to differentiate between bots and legitimate accounts.
Answers to these questions will help you understand whether a graph database is the right fit for you.
Here are some popular graph database examples:
A relational database stores data in the form of tables. Tables function similarly to physical file cabinets, as each folder contains information on a topic.
A relational database connects data according to logical relationships. Because data is recorded in tables, it has a logical and straightforward structure. Rows are unique records with identifiers; columns identify the attributes of a data set. The tables are then joined together using relationships.
Let’s say you wanted to gather all of the buyers who live in a specific city. You start with your "customers" table. Then you go to your "cities" table and look for the city you're interested in. Finally, you combine the two tables to find all the customers residing in that city.
Relational databases are critical for applications that need structured data storage and transactional integrity. They are suitable for:
Relational databases have a standardized schema. They’re a suitable option when:
Here are some of the common relational database examples:
Both graph and relational databases store bits of data related to each other in particular ways. However, they represent the relationships between these pieces of information quite differently.
A graph database organizes data as a network of entities and relationships. It uses mathematical graph theory to store and operate on data relationships. They model these relationships more effectively than relational databases, significantly improving application performance.
Relational databases hold information in tables with rows and columns. Unlike graph databases, they tend to become inefficient in operations involving complex data relationships because they require several data table lookups.
Here, we examine the key differences between graph database vs relational database through five aspects.
Conversely, graph databases use nodes and edges to represent data with flexible relationships, showing them as they behave in the real world. Since the importance of relationships increases in graph databases, you can connect data more naturally.
A graph database allows you to change the schema as your database expands.
Graph databases use Cypher query language. Cypher’s syntax and functionality make it appropriate for querying many-to-many relationships. Think of Cypher as a way to map English language sentence structure to patterns in a graph where : nouns usually stand in for nodes; verbs for relationships;and adjectives and adverbs for properties.
Users may find it hard to scale efficiently with relational databases. As data grows, it becomes more distributed, disrupting the structure. Using multiple servers affects database performance and impacts application response time and availability.
Even though creating and querying graph databases can become more complex, they work faster on complex, relationship-based data. However, poorly constructed graph queries can drag down performance if you’re not careful. Graph databases may take your team through a learning curve since they’re still evolving.
Relation (in relational databases) is table with rows and columns, representing a set of tuples. Relationships between data are defined through foreign keys.Whereas, a graph is a collection of nodes (entities) connected by edges (relationships). Graphs are more flexible and can represent complex and dynamic relationships.
No, SQL (Structured Query Language) is the standard language for querying and manipulating data in relational databases.
No, MongoDB is a document-oriented database. While it can store and query documents with embedded relationships, it's not specifically designed for efficiently handling complex graph structures and traversals.
No, PostgreSQL is a powerful relational database. While it can be used to model some graph-like structures, it's not as efficient or optimized for graph traversals as dedicated graph databases.
Choosing between a graph and a relational database depends on your requirements and use case. Evaluate the purpose for which you intend to use the database. If it’s a use case where relationships between data matter more, like in social networks or product recommendations, go with graph databases.
On the other hand, when your data is more structured, a relational database supports you easily.
Learn more about relational databases and understand why database engineers prefer working with them.