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Table of contents
1.
Introduction
2.
Components of Graph Database
3.
Graph Models
3.1.
Property Graphs
3.2.
RDF Graphs
4.
Properties of Graph Database
4.1.
Storage
4.2.
Index-free Adjacency
5.
Use Cases
5.1.
Recommendation engines
5.2.
Fraud detection
5.3.
Semantic Search
5.4.
Network Management
5.5.
Digital Content Management
6.
Some Popular Graph Databases
6.1.
Neo4j
6.2.
DGraph
6.3.
JanusGraph
7.
Advantages of Graph Databases
8.
Disadvantages of Graph Databases
9.
FAQs
9.1.
What are Graph Databases?
9.2.
What are the different graph models?
9.3.
Name some popular graph databases.
9.4.
What is the advantage of graph databases?
9.5.
What is the disadvantage of using graph databases?
10.
Conclusion
Last Updated: Mar 27, 2024
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Introduction to Graph Databases

Author ANKIT KUMAR
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Master Python: Predicting weather forecasts
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Ashwin Goyal
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Introduction

A graph Database is sometimes also called a semantic database. It is a software application that stores, queries, and modifies network graphs. A network graph is a graphical representation of nodes and edges. Each node represents an entity (for example, a person), and each edge represents a connection or relationship between two nodes.

A graph database is a type of NoSQL database system that is based on a topographical network structure. Graph databases provide a more conceptual view of data that is more closely related to reality.

Components of Graph Database

The fundamental components of graph databases are:

  • Nodes: These are data instances or entities that represent any object that needs to be tracked, such as people, accounts, locations, and so on. It is equivalent to rows in a relational database.
  • Edges: These are the key concepts in graph databases that represent node-to-node relationships. The connections are either unidirectional (one way) or bidirectional  (two way). These are equivalent to foreign keys in a relational database.
  • Properties: It represents descriptive information about nodes. Edges, in some cases, have properties as well.

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Graph Models

There are two popular models of graph databases: property graphs and RDF graphs. 

Property Graphs

A property graph is made up of vertices that contain detailed information about a subject and edges that represent the relationship between the vertices. The vertices and edges can have attributes, known as properties, that are associated with them.

It is also known as a labeled-property graph. Property graphs are used in a wide variety of industries and sectors due to their versatility, including finance, manufacturing, public safety, retail, and many others.

RDF Graphs

RDF stands for Resource Description Framework. A statement is represented in the RDF model by three elements: two vertices connected by an edge that reflects the subject, predicate, and object of a sentence—this is known as an RDF triple. A unique URI, or Unique Resource Identifier, identifies each vertex and edge. The RDF model enables information exchange by allowing data to be published in a standard format with well-defined semantics.

Properties of Graph Database

The key properties of a graph database are:

Storage

Instead of tables, a graph database represents information using nodes, relationships between nodes, and key-value properties. This model is typically much faster for associative data sets and employs a schema-less, bottom-up approach that is ideal for capturing ad hoc and rapidly changing data. Because data relationships can be elegantly captured and represented in graph databases, today's interconnected data can be easily stored in a graph database.

Index-free Adjacency

The graph databases use index-free adjacency. This means that each node refers to its neighbours directly, acting as a micro-index for all nodes nearby. Each node has a direct physical RAM address. Because query times are proportional to the amount of the graph searched, rather than increasing with the overall size of the data, index-free adjacency is less expensive and more efficient than index-based adjacency.

Use Cases

There are various use cases of the graph databases. A few of them are stated below.

Recommendation engines

Real-time product and e-commerce recommendations improve the user experience while increasing profits. A graph database can be used to make product recommendations to a user based on what products others who follow the same sport and have similar purchase history have purchased.

Fraud detection

You can use fast graph queries to detect that, for example, a potential purchaser is using the same email address and credit card as a known fraud case. Graph databases can also assist you in detecting relationship patterns, such as multiple people associated with the same personal email address or multiple people sharing the same IP address but residing at different physical addresses.

Semantic Search

Semantic searches aid in providing meaning behind keywords, resulting in more relevant results that are easier to map using graph databases.

Network Management

Networks are linked graphs. Graphs shorten the time it takes to notify a network administrator of a problem in a network.

Digital Content Management

The amount of digital content available is massive and growing all the time. Graph databases provide a scalable and simple database model for tracking digital assets such as documents, evaluations, contracts, and so on.

Some Popular Graph Databases

Some popular graph databases are:

Neo4j

It is written in Java with native graph storage and processing. Some of the features of Neo4j are:

  • It is scalable.
  • It follows a Property Graph data model.
  • It supports SQL-like query language known as CQL.

DGraph

DGraph (Distributed Graph) is an open-source distributed graph database system designed with scalability in mind. Some of the features of DGraph are:

  • DGraph is an open-source system.
  • It supports the query language GraphQL, which is designed for APIs.

JanusGraph

JanusGraph is an open-source, distributed, and scalable graph database system with numerous integration options for big data analytics. Some features of the JanusGraph are:

  • It uses the graph transversal query language Gremlin.
  • It supports multiple options for storing the graph data, such as HBase.
  • It supports ACID transactions.

Advantages of Graph Databases

The advantages of graph databases are:

  • The graph database provides a more flexible platform for locating distant connections or analysing data based on factors such as relationship strength or quality.
  • Graph databases are a very powerful tool when it comes to handling interconnected data.
  • Graph databases can perform real-time updates on large amounts of data while also supporting queries.

Disadvantages of Graph Databases

  • There is no standardised query language for graph databases.
  • Not suitable for transactional-based systems.
  • Very small user base. Therefore support is limited.

FAQs

What are Graph Databases?

It is a software application that stores, queries, and modifies network graphs. A graph database is a type of NoSQL database system that is based on a topographical network structure. 

What are the different graph models?

The two types of graph models are Property graphs and RDF graphs.

Name some popular graph databases.

Some popular graph databases are Neo4j, DGraph, JanusGraph, etc.

What is the advantage of graph databases?

The graph database provides a more flexible platform for locating distant connections or analysing data based on factors such as relationship strength or quality.

What is the disadvantage of using graph databases?

It does not have a standardised query language, and it is also not suitable for transactional-based systems.

Conclusion

In this article, we have extensively discussed the Graph Databases and their features and characteristics.

  • A graph database is sometimes also called a semantic database. It is a software application that stores, queries, and modifies network graphs. A network graph is a graphical representation of nodes and edges.
  • The fundamental components of graph databases are nodes, edges, and properties.
  • The graph database provides a more flexible platform for locating distant connections or analysing data based on factors such as relationship strength or quality.

We hope that this blog has helped you enhance your knowledge regarding Graph Databases and if you would like to learn more, check out our articles here. You can also try the guided path here. Check out the articles related to databases here. Do upvote our blog to help other ninjas grow. Happy Coding!

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