What is a Graph Database?

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Graph databases are NoSQL systems created for  investigating correlation within intricately related things. By emphasizing on the data relationship more, the structure overcomes the drawbacks of relational databases.

This article explains what graph databases are and how they work.

What is a Graph Database?

A graph database is a specialized NoSQL database created for the storage and querying of data connected by predefined relationships based on a topographical network structure. Graphs represent data sets using nodes, edges, and properties. 

  • Nodes or points are instances or entities of data which represent any object to be tracked, such as people, accounts, locations, etc.
  • Edges or lines are the critical concepts in graph databases which represent relationships between nodes. The connections have a direction that is either unidirectional (one way) or bidirectional (two way).
  • Properties represent descriptive information associated with nodes. In some cases, edges have properties as well.

Example of Graph Database

The most common example of graph databases is the Facebook Friend relationship as well as the Like relationship. You can see some of that in the graphic below from Neo4j.

Image source: Neo4j

Why Graph Databases?

Graph databases improve performance by several orders of magnitude. Moreover, the graph databases address big challenges many of us tackle daily. Major reasons of using graph database is it:

  • Navigate deep hierarchies,
  • Find hidden connections between distant items, and
  • Discover inter-relationships between items.
  • Supports simple modeling
  • Uses structured or unstructured data
  • Simplifies querying

How Does Graph Databases Work?

Graph databases work on relationship principles.With graph databases, both the data and the relationships between the data are treated equally. Physical connections between related nodes are made, and this physical link is likewise considered to be a piece of data.

By modeling data in this way, it is possible to query relationships in the same way as querying the data itself. Instead of calculating and querying the connection steps, graph databases read the relationship from storage directly.

In terms of agility, performance, and flexibility, graph databases are more related to other NoSQL data modeling methods. Graphs don’t have schemas, just as other NoSQL databases, which makes the model flexible and simple to change over time.

Graph Database Use Cases

There are many notable use cases where graph databases outperform other database modeling techniques, some of which include:

  • Real-Time Recommendation Engines:  Real-time product and ecommerce recommendations provide a better user experience while maximizing profitability. Notable cases include Netflix, eBay, and Walmart.
  • Master Data Management:  Linking all company data to one location for a single point of reference provides data consistency and accuracy. Master data management is crucial for large-scale global companies.
  • GDPR and regulation compliances: Graphs make tracking of data movement and security easier to manage. The databases reduce the potential of data breaches and provide better consistency when removing data, improving the overall trust with sensitive information.
  • Digital asset management: The amount of digital content is massive and constantly increasing. Graph databases provide a scalable and straightforward database model to keep track of digital assets, such as documents, evaluations, contracts, etc.
  • Context-aware services: Graphs help provide services related to actual-world characteristics. Whether it is natural disaster warnings, traffic updates, or product recommendations for a given location, graph databases offer a logical solution to real-life circumstances.
  • Fraud detection: Finding suspicious patterns and uncovering fraudulent payment transactions is done in real-time using graph databases. Targeting and isolating parts of graphs provide quicker detection of deceptive behavior.
  • Semantic search: Natural language processing is ambiguous. Semantic searches help provide meaning behind keywords for more relevant results, which is easier to map using graph databases.
  • Network management: Networks are linked graphs in their essence. Graphs reduce the time needed to alert a network administrator about problems in a network.
  • Routing: Information travels through a network by finding optimal paths makes graph databases the perfect choice for routing.

What are the types of graph databases?

Graph databases are often broken down into two main types by their data model: 

RDF graphsProperty graphs
RDF graphs (RDF stands for Resource Description Framework) are designed to conform to W3C (World Wide Web Consortium) standards. It’s a shift from storing data as a relational database. It expresses information in graphs using 3 parts: object, predicate, and subject.Property graphs are more versatile representations, so they’re more commonly used across various industries. A property graph models relationships among data points, with detailed information about the subject and how that data interrelates.

What are the advantages of graph databases?


A graph database delivers better efficiency when working with data that is primarily relational, as well as the consistency you need as your data grows. Even as your data volume grows, a graph database is a fantastic solution for real-time queries including large data processing.

Better problem-solving

Problems can be solved more effectively with a graph database than they can with relational databases. Before you lock it in, take into account speculative circumstances that will provide your interconnected data with the most useful situation for a graph database.

AI and machine learning friendly

The use of graph databases in machine learning and artificial intelligence is a natural fit. By discovering patterns and relationships between your data that might otherwise go unnoticed, a graph database can help you discover insightful business information. You can quickly train models and make predictions on your data by employing a graph database as a scalable data storage.

Some examples of problems that can be solved by combining a graph database with machine learning would be finding valuable steps in customer acquisition journeys, personalizing services and platforms, finding users across multiple platforms, fraud prevention by finding non-obvious but connected behavior, and much more.

Object-oriented thinking

With a graph database, there are no hidden assumptions. The semantics are clear and explicit. With object-oriented thinking, you have the fine control to keep the data in place without hidden assumptions.


You have a flexible platform to find connections using a graph database. Your data can be examined based on its strength or quality as compared to other data in your database. As your application expands, you may simply add new properties or node types without having to worry about the schema changing.

Accessible recursive path queries

With a graph database, you can use practical queries to discover both direct and indirect connections between data. When you group the searches and search for patterns relating to your product and how it interacts with audience data, that level of accessibility is crucial.

Multiple dimensions

You can manage big data with a graph database by combining and hierarchizing multiple dimensions. So, you could segment a group based on different dimensions: time, demographics, geo dimensions, and more.

Aggregate queries

A graph database makes it simple to combine and organize pertinent/related data in ways that relational databases cannot. So, nearly any analytical query may be run on a graph database by business analysts and data scientists.

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