XenonStack Recommends

Enterprise Data Management

Graph Database Architecture and Use Cases

Chandan Gaur | 28 March 2023

Graph Database Architecture and Use Cases

Introduction to Graph Database

Graph Database is simply an online database management system providing create, Read, Update, and Delete (crud) operations that expose the graph data model and is a collection of nodes and edges, where each node represents an entity, and each edge represents a connection or relation between two nodes.
  • A unique identifier describes each node in a GDB, a set of incoming/outgoing edges, and a set of properties expressed as key-value pairs.
  • A database for storing, managing, and querying highly connected and complex data.
  • Schema-less, based on graph theory
  • Only two types of data inside it i.e., nodes and relationships.
  • One of the NoSQL database management systems, queries by several languages, depends on database products.
  • Widely used in the social network system.
A database that uses graph architecture for semantic inquiry with nodes, edges, and properties to represent and store data.” Click to explore about, Role of Graph Databases in Big Data Analytics

Graph Vs. Relational Database

Type Relational DB Graph DB

Tables with Rows and Columns

Nodes, Relationships, Labels, Properties


Related across tables, set up utilizing foreign keys between tables

Represented by relationships between edges and nodes


Relational - Example of a demo Students and Department tables


Graph - example of a demo Person and 3 departments as nodes


Pros & Cons 1. Rigid Schema
  • 2. High performance for transactions
  • 4. Poor performance for deep analytics
  • 5. Require complex joins between tables

1. Flexible Schema
2. High performance for complex transactions
3. High performance for deep analytics
4. Do not require joins

Query Pattern SQL Statement:

SELECT name FROM Person

LEFT JOIN Person_Department

  ON Person.Id = Person_Department.PersonId

LEFT JOIN Department

  ON Department.Id = Person_Department.DepartmentId

WHERE Department.name = "IT Department"
Cypher Statement:

MATCH (p:Person)-[:WORKS_AT]->(d:Dept)

WHERE d.name = "IT Department"

RETURN p.name
Top Use Cases

Transaction-focused use cases, including online transactions and accounting

Relationship-heavy use cases, including fraud detection and recommendation engines

What are the types of Graph Databases?

Popular Graph databases are mentioned below:

  1. Neo4j
  2. Microsoft Azure CosmosDB
  3. OrientDB
  4. ArangoDB
  5. Virtuoso
  6. JanusGraph
  7. Amazon Neptune
  8. GraphDB
  9. Giraph
  10. AllegroGraph

There are two popular types of Graph databases, Property graphs and RDF graphs. The property graph is used for querying and analytics, and the RDF graph emphasizes data integration.

Property Graphs

The vertices contain itemized information about a subject, and edges indicate the connection between the vertices. The vertices and edges have attributes, which are known as properties. Property graphs are essentially used to demonstrate relationships among data, and they enable query and data analytics based on these relationships. This is utilized in many businesses and areas, like finance, manufacturing, public safety, retail, and numerous others.

RDF Graphs

RDF stands for Resource Description Framework that conforms to W3C (World Wide Web Consortium) standards designed to represent statements. They are best for representing complex metadata and master data. RDF has features like data merging, even if the underlying schemas differ. It explicitly upholds the evolution of schemas over the long run. RDF has a unique terminology for naming nodes and edges in a graph. An edge is known as a triple, the source node is known as a subject, the edge name is known as a predicate, and the target node is known as an object. The RDF model empowers an approach to distribute the data in a standard format with distinct semantics, which permits data exchange. RDF graphs are widely adopted and used in Government statistics agencies, pharmaceutical organizations, and medical care sectors.

Explore  more about Composable Data Processing with a Case study
Let's talk about Neo4j properties -
  • Neo4j has a robust property
  • Fully transactional (Atomicity, Consistency, Isolation, Durability)
  • Highly agile.
  • Highly scalable, up to several billion nodes/properties/relationships.
  • Very fast when it comes to querying connected data.

Why are Databases important?

The importance of databases is mentioned below:


One of the reasons for choosing a graph database is that sheer performance increases when dealing with connected data versus RDBs and NoSQL stores. In RDBs, where join-intensive query performance degrades as the dataset grows more prominent, with a graph database, performance tends to remain approximately steady even as the dataset grows. The reason for this better performance of the Graph is that queries are restricted to a part of the Graph. As a result, the execution time for every query is proportional to the size of the Graph traversed rather than the overall size.


Graphs are additive, we can add new relationships, new labels, new subgraphs and also new nodes to an existing structure without disturbing the current queries and application functionality.


Growing with graph database aligns perfectly with test-driven development practices, allowing graph database backend to develop in step with the rest of the application and any growing business requirements.

Production Recommender system is a useful information tool based on algorithms to provide customers with the most suitable products.” Click to explore about, Product Recommendation with Graph Database

The Advantages of Graph Database technology?

The graph format provides a flexible platform for discovering distant connections and dissecting data based on the strength or quality of relationships. It allows you to explore patterns and connections of social networks, IoT, big data, and complex transaction data for various business use cases.

Graph databases store the relationships, queries, and algorithms, utilizing the connectivity between vertices, which tends to run in sub-seconds. In other cases, it might require hours or days. Forget about countless joins, and also, the data can be more easily used for analysis and machine learning. Complex relationships can be evaluated easily for deeper insights with the help of graph format. Graph databases run queries mostly in Property Graph Query Language (PGQL). Furthermore, a couple of more broadly acknowledged query languages are SPARQL, GraphQL, Gremlin, and Cypher.

In precise Graph databases:

  1. Finds the shortest path between two nodes.
  2. Determines the nodes that create the most activity.
  3. Analyze connectivity to identify the weakest points of a network.
  4. Analyze the state of the network or community based on connection density in a group
Read More about Data Catalog with Data Discovery

What makes Graph Database unique?

A lot of DB's have similar features and characteristics but what makes a graph database (GDB) unique is the mentioned two factor -

  • The underlying storage - Certain graph databases use native graph storage that Is optimized and developed for handling and storing Graph. Not all the database use native graph storage, some serialize the graph data into a relation Database or object-oriented database or some other general purpose data stores.
  • The processing engine - Some definition says that databases uses index- free adjacency means that connected nodes physically point to each other in Database. But let's take a broader view: any database that behaves like the GDB (i.e., exposes a graph model through (CRUD operations) from the user's perspective qualifies as a graph database. Native graph processing or index-free adjacency benefits traversal performance, but at the expense of making some queries that do not use traversals difficult.

How do Graph Databases work and their Use-cases?

Relationships get priority in graph databases, unlike other database management systems. In graph databases, connected data is equally or maybe sometimes more critical than individual data points.

This connections-first approach to data means relationships and connections are persisted through every part of the data lifecycle, starting from the idea, logical model designing, actual model execution, query language operations, and persistence within a scalable, reliable database system.

This approach enables your application by not inferring data connections using foreign keys or out-of-band processing, like MapReduce. Hence by this, data models are simpler and more expressive than the ones you’d produce with relational databases or NoSql.

the below highlighted are the Use Cases of Graph Database

Fraud Detection

Fraud detection in Facebook and banks seems to work well. They Send notice to you when there is suspicious activity on your account.The fraud detection use case can be applied to cybersecurity intrusion detection as well. It has the benefit of sending analysis to look at only events that are significant and not Wasting resources on outlier as it uses intelligence tools, not hard-coded thresholds.

Identity and Access Management

This is a use case mentioned by Neo4j. It holds information about parties (like administrator, business units, end-users) and resources (like files, shares, network devices, agreements) for determining authorization that who can access and who can't access or manipulate resources By tracing from the individual through groups, roles, and product.

Network and IT operations

This is a default use case as network topology looks Like a graph, so makes sense to a Graph DB model.

Knowledge Graph

Google is one who uses this model. In this, documents from different sources are collected up and then presented to the user in a way that is searchable.

Java vs Kotlin
Our solutions cater to diverse industries with a focus on serving ever-changing marketing needs. Click here for our Graph Database Managed Solutions

A Comprehensive Approach

The real world is more interconnected, and graph database Provides the best infrastructure to link diverse data. As it has easy expressions for the relationship between data, so it makes it easier for the programmer, user, and machine to understand the data and to find insights. Want to know more about databases we advice taking the subsequent steps -