Relational Database vs. Graph Database

In the digital age, data management has become a crucial aspect for businesses of all sizes. While relational databases have long been the standard for organizing and storing information, the emergence of graph databases has opened up new possibilities for handling complex data relationships. Our article, «Relational Database vs Graph Database,» provides a comprehensive comparison between these two types of databases. It covers key differences, advantages, disadvantages, and appropriate use cases for each, helping you determine which database type may be best suited to your specific needs. From understanding the fundamentals of each database to exploring case studies that illustrate their practical applications, this guide will equip you with the knowledge to make informed decisions about your data management strategy.

The business landscape has changed dramatically in recent years, and organizations of all sizes are under pressure to make better use of the massive data they are gathering. Database management systems play a vital role in this process, and as businesses grapple with the urgency to make better use of their data, the importance of database management systems is skyrocketing. This is demonstrated by the quickly rising global Database Management Systems market, which is expected to grow at a CAGR of 12.2 percent and reach 142.7 trillion by 2025. That’s huge!

However, many organizations are discovering that picking the best database model for some jobs is not that easy when developers are faced with more than one option. Relational database vs. graph database is one such dilemma. What’s the difference, do you go for relational or graph database and why? Let’s find out.   

What is a graph database?

A graph database is a NoSQL database that uses graph structures to store data. This makes it easy to represent relationships in complex and highly interconnected data, which is useful for applications like social networking and recommendation engines. Graph databases are often well-suited for applications that need to perform complex data analysis, as they can easily traverse large data sets to find deep insights. Additionally, graph databases can be used to store geographical data, making them useful for applications like mapping and route planning. These are just a few examples. 

Nodes store data entities, while edges store relationships. An edge will contain a start, end node, direction, and type.  

How does a graph database work?

A graph database works by using graph structures to run logical queries. It provides a graph model that represents relationships in a data set. Graph databases allow users to run semantic queries on data. The querying involves running algorithms to determine various relationships, including influencers, paths, patterns, communities, and single points of failure. 

This type of querying enables efficient analysis of large amounts of data. It can link disparate data sources and provide useful insights into a data set.  

Graph databases have three main components that you need to know to understand how they work.

• Nodes: A node is the data point that stores information about a particular entity.. The nodes themselves can represent anything, from people to products to locations. The important thing is that the nodes are connected in a way that allows for queries and analytics. In most cases, a node will represent a single object, such as a person or a place. For instance, a node in a social network might represent an individual user. Nodes can contain any type of data, including text, images, and numerical values.
• Properties: Properties are key-value pairs that are used to describe the entities in the graph. The values can be any data type, including strings, numbers, Booleans, and dates. Properties can be single-valued or multi-valued, and they can be used to represent both simple and complex relationships. For example, a product node may have properties like color, size, material, and other properties.
• Edges: Edges are the relationships between nodes i.e the lines that connect the nodes. Edges can be directed or undirected. Directed edges have a direction, meaning the edge goes from one node to another node, and there is a specific order to the nodes. An undirected edge, on the other hand, does not have a direction. This means that the edge can go from any node to any other node, and there is no specific order to the nodes. Edges can also be weighted or unweighted. Weighted edges have a value associated with them, while unweighted edges do not. 

The design and architecture of a graph database enable it to offer better performance because of its flexibility, compared to a relational database. Graph databases use index-free adjacency, meaning each node on a disk has pointers to connected nodes. Therefore, the database doesn’t store large indexes in RAM because it’s available through the node. This enhances performance, which will depend on the number of traversing relationships.   

What is a relational database?

A relational database is a database that stores data in the form of tables. Tables are similar to folders in a file cabinet, where each folder contains information about a specific topic. In a relational database, each table is related to other tables in the database. For example, a table of customer information might be related to a table of orders. This relationship between tables allows data to be linked together, making it easy to retrieve and update information. Because of this, relational databases are very popular for storing large amounts of data. However, they can be more difficult to set up and maintain.

How does a relational database work?

A relational database works by connecting data based on logical relationships. Data is stored in a table, which creates a logical and simple structure. The main attributes of a table are rows, which are unique records with their identifiers, and columns, which identify the attributes of a data set.  The tables are then linked together by relationships.

For example, if you were looking for all the customers who live in a certain city, you would first access the «customers» table. Then you would access the «cities» table and look for the city that you're interested in. Finally, you would link the two tables together to find all the customers who live in that city. 

The graph data model vs the relational data model

A graph data model describes an arbitrary data set, called a domain, as a connected graph of data entities (nodes) and relationships. The model also has properties and labels. This data model shows how different items in a domain connect. While a relational model re-formats the structure to fit in a table, a graph model maintains the same format as it would have been drawn on a whiteboard. 

A relational data model basically re-formats data to fit into a normalized table structure. It organizes data into rows and columns and stores it in a table. The table is the primary data model; thus, it is widely used for data processing and storage.   

Popular types of graph databases

There are several types of graph databases. Here are some of the most popular ones:

1. Property graphs

Property graphs focus on running queries and data analytics. They model relationships between different data points and provide detailed information on the subject and how the data is interconnected. Vertices contain the information, while edges represent the links between the vertices. They also enable data analytics and querying based on these relationships. 

Since a property graph is more versatile, it is used in various industries, including manufacturing, retail, public safety, and finance. 

2. RDF graphs

Resource Description Framework (RDF) graphs focus on data integration. They follow the Worldwide Web Consortium (W3C) standards. Therefore, an RDF graph is best suited for representing complex data especially on the web. 

RDF graphs are used for knowledge graphs, data integration, and linked data. They represent complex concepts or provide inference and rich semantics on data.

3. Hypergraph

A hypergraph is a graph database with a hyperedge relationship. A hyperedge relationship connects multiple nodes by allowing several nodes at either end of the relationship. This graph is useful when a dataset contains numerous many-to-many relationships. Hypergraphs are more generalized because hyperedges are multi-dimensional.

4. Triple store

A triple-store graph database stores data in a subject-predicate-object format. A separate node will represent any additional information. Each notation in a triple store graph has two nodes, one for the subject and another for the object, while an arc represents the predicate.

This approach has several advantages. First, it makes it easy to store complex relationships between pieces of information. Second, it allows for quick and easy retrieval of information. Finally, it makes it possible to update information in the database without having to rebuild the entire database from scratch. As a result, triple store graph databases are well-suited for applications where data is constantly changing or where relationships between pieces of information are frequently updated.

5. Key-value store

A key-value store graph stores data in key-value pairs. The keys and values can be simple or complex compound objects. These databases use primary and foreign keys. The primary key is the unique identifier, while the foreign key is the field that provides the linkage.    

Since a key-value database is highly partitionable, it can allow horizontal scaling to high levels that other types of databases may not achieve. They are commonly used in e-commerce websites as shopping carts and in web application sessions. 

Major differences between graph database and relational database

Relational databases differ from graph databases in the following main ways.

1. Data storage

A relational database stores data in a simple, tabular format. Data is organized into rows and columns, and each row contains information about a single entity. Different tables are interlinked using JOINS for fast querying. In contrast, a graph database stores data entities as nodes, and relationships between those entities are represented as edges.

2. Purpose

Relational databases serve both analytical and operational purposes. Examples of operational functions include tracking customer orders or inventory levels. Examples of analytical functions include identifying trends or understanding customer behavior.

However, graph databases are suited for analytical purposes. They use a graph structure to model relationships between data points, making it easy to uncover hidden patterns and insights.

3. Performance

Relational databases have been the traditional choice for storing and managing data. However, they are not well-suited for big data analysis due to their slow performance. In contrast, graph databases were designed specifically to handle large-scale data analysis quickly and efficiently. They basically solve the shortcomings of relational databases. 

4. Schema

Relational databases have a rigid schema. They store data in a pre-defined data structure and format, which makes them inflexible. On the other hand, graph databases are schema-free and can comfortably handle unstructured data. This provides flexibility and provides an opportunity for diverse applications in numerous fields.  

5. Maintenance

Maintaining a relational database can be a challenge due to the rigid schema and data structure. A minor change can affect the entire data structure, and it can be difficult to keep track of all the relationships between different tables. On the contrary, maintaining a graph database is much simpler because it is schema-free. 

This means that you don't have to worry about the data structure becoming invalid if you make a change, and it's easy to find all the information you need since everything is connected.

Check out this summary of the key differences between relational database vs. graph database.

Advantages of graph database over relational database

Graph databases have numerous benefits over the relational databases. Here are the top advantages:

1. Performance

Graph databases offer better performance when dealing with data systems with highly connected data. They provide consistency, which is critical as data continues to expand. As data grows, there is a need to run more real-time queries involving big data analytics, which relational databases are unsuitable for. Graph databases provide the best solution here.

The performance of graph databases allows them to solve problems better and in ways other database models cannot. A good example is a hypothetical situation where your interconnected data would be most useful in a graph database.

2. Artificial intelligence (AI) and machine learning (ML) friendly

Graph databases allow users to discover very complex connections, relationships, and patterns between data, which other models would have missed. This provides valuable business insights and builds a scalable data store that can be used to train models and make predictions. Therefore, graph databases work perfectly well with AI and ML.

Combining machine learning and graph databases has enabled companies to understand their customers and thus personalize their services and platforms accordingly.  This ability is what has enabled graph databases to play a huge role in areas such as fraud detection, where the combination of ML and graph databases is now being used to identify non-obvious but connected behavior. 

3. Flexibility

Graph databases provide a flexible platform for manipulating and discovering relationships. Users can analyze data depending on its strength and quality. These databases also allow users to add node types and properties as data grows without concern for schema data changes.  

This level of flexibility allows you to manage big data by merging and ranking multiple dimensions. This enables segmenting of data based on various aspects. 

4. Object-oriented thinking

A graph database has clear and explicit semantics that eliminate any hidden assumptions that can lead to errors. This enables object-oriented thinking with proper control over your data. 

5. Query aggregation

Data scientists and analysts can run nearly all analytical queries on graph databases. Therefore, they can aggregate and categorize data in ways that aren’t possible with relational databases. Graph databases provide a high level of accessibility that allows users to bundle queries together to determine hidden relationships and patterns.    

Conclusion 

Data collection, processing, and analysis is now critical to the growth and survival of any business, and it’s nearly impossible to find other tools that can do this better than databases. They make it easier for business managers to manage data and derive important information that aids their decisions. However, you’ll still need to decide which database model best fits your needs at different times.

Relational databases are no doubt the best when data is highly structured and when relationships between data points are relatively simple. For example, a customer database for a retail store would typically be relational, as the data is easy to store in rows and columns. However, when data is more complex or when relationships between data points are more intricate, a graph database may be a better choice. Social networking sites are a good example of when to use a graph database, as the connections between users can be represented as nodes and edges in a graph. Similarly, recommendation engines often use graph databases to model the relationships between items in order to make better recommendations.

Ultimately, the decision of whether to use a relational database or a graph database depends on the nature of the data being stored. For highly structured data with simple relationships, a relational database is usually the best option. However, for more complex data with intricate relationships, a graph database should be the better choice.