10 Features of Graph Database in NoSQL: A Beginner’s Guide

Many organizations rely on advanced analytics to derive deeper insights from their data assets. Unlike traditional relational models, graph-based databases provide the flexibility and performance required to analyze interconnected data points and discover hidden patterns. These databases have become essential for optimizing complex data relationships and efficient decision-making.

With businesses recognizing the strategic importance of utilizing graph models for improved data management and analytics, adoption rates continue to increase. The graph database market size is expected to expand from USD 3.47 billion in 2024 to USD 10.9 billion by 2032. This growth is driven by the accelerating demand for data-driven insights across different industries.

If you are ready to explore the power of graph databases, you’re in the right place. This article serves as a comprehensive guide to the key features of graph technology, its types, and real-world use cases.

Introduction to Graph Database in NoSQL

A graph database in NoSQL is a type of data model that utilizes topographical network structures to store and represent data. Compared to other types of databases, which store data in tables, graph databases use the following core components:

• Nodes: A node represents entities or data objects such as users, products, or locations. Each node can have unlimited numbers and types of relationships with other nodes.‍
• Edges: Define relationships between nodes, like purchased or visited.‍
• Properties: These are additional information or attributes that describe the nodes and edges. For example, a user name, email, or transaction amount.

These components within a graph database allow you to handle and query complex relationships between nodes. Here’s an example to understand this better:

In the illustration above, a Person is a node with properties like Name, Email, and DOB. There are two relationships between the nodes Rachel Jones and David, which are COLLEAGUE_OF (with property Duration) and FRIEND_OF. Similarly, other nodes in the graph may also hold one or more uni-directional or bi-directional relationships.

10 Features of Graph Database in NoSQL

A graph-based database in NoSQL offers flexibility in representing complex data structures while maintaining good performance. Here are some key features of this database that highlight its potential:

Optimized Relationship Handling

A graph database makes it easier to store and manage one-to-one, one-to-many, many-to-one, or many-to-many relationships. You can directly symbolize these associations as edges between nodes. Using such an efficient structure allows you to navigate through sophisticated connections without performance degradation.

For example, social media platforms like Facebook use graph databases to manage user connections. Consequently, when you view a friend’s profile, the platform quickly retrieves all mutual friends using direct relationships between users.

Flexible Data Modeling

One of the greatest benefits of using a graph database is the ability to evolve your schema dynamically. As your business requirements change, you can easily adapt the structure of your graph without downtime or extensive migrations. With this flexibility, you do not have to restructure the entire database whenever a new data type or relationship is introduced.

Scalability

Graph databases are designed to manage large volumes of interconnected data. With horizontal scaling, you can distribute the data across multiple nodes, ensuring your system remains responsive even as the dataset grows.

For instance, LinkedIn utilizes graph databases to deal with connections among over 700 million users. The platform scales with the addition of new connections and data points across distributed nodes.

High Availability

Graph databases ensure high availability by replicating data across multiple machines. Replication strategies involve storing copies of each partition of the graph on different nodes. If a node fails, a replica on another node can immediately take over, preventing any service interruptions.

No Joins Required

In a traditional relational database, SQL joins are required to combine data from different tables. In contrast, graph databases do not need joins because of the direct relationships between data entities or nodes. This results in faster query processing and a simplified data structure.

For instance, in a knowledge management system, a graph database can help retrieve related documents, keywords, and authors. This is possible by following direct relationships between nodes, bypassing the need for joins.

Indexing

Indexing is a powerful technique to optimize query performance in graph databases. It enhances efficiency by enabling fast access to data during traversals, searches, or operations involving vertices and edges. Instead of scanning the entire dataset, indexes help you quickly locate the data you need, saving both time and computational resources.

Massively Parallel Processing (MPP)

MPP is a robust feature in graph databases, and it is designed to handle large-scale data operations at high speed. With MPP, you can divide complex tasks into smaller, independent operations and process them simultaneously across multiple processors or nodes. By leveraging different machines, MPP helps balance the workload and reduce the risk of overloading a single node.

Supports Data Integration

Graph databases, such as Neo4j, can help you manage and integrate dispersed big data from heterogeneous sources into a unified graph structure. However, such graph models do not enforce data quality through schemas, leaving that responsibility to the application layer.

By using a third-party data movement platform like Airbyte, you can streamline this integration process, ensuring seamless data flow and consistency across different systems.

With 550+ pre-built connectors, Airbyte allows you to extract data from diverse sources and load it into the destination of your choice. If a suitable connector for graph databases isn’t available, you can easily build one with Airbyte’s no-code Connector Builder featuring an AI assistant.

Here are a few more features of Airbyte that help in data management for graph databases:

• Change Data Capture (CDC): Airbyte supports CDC to help you capture the latest modifications from your source system and replicate them into the destination.‍
• Streamline GenAI Workflows: You can load unstructured data into any of the Airbyte-supported vector databases, including Pinecone, Weaviate, and Milvus. These vector stores represent the data in high-dimensional embedding space, making them suitable for handling the data used by the Large Language Models (LLMs).‍
• Developer-friendly Pipeline: You can utilize the Airbyte connectors within Python workflows through its open-source library, PyAirbyte. This allows you to build and customize developer-friendly pipelines for smooth data integration.‍
• Multiple Sync Modes: Airbyte provides different synchronization modes, including Full Refresh | Overwrite, Full Refresh | Append, Full Refresh | Overwrite + Deduplication, Incremental | Append, and Incremental | Append + Deduplication. These modes can help you read data from a source system and write it to your target system.

Low Latency and High Throughput

Graph databases provide low latency and high throughput while processing queries on large datasets. With low latency, your queries execute almost instantly, enabling real-time analytics and faster decision-making.

On the other hand, high throughput ensures that you can manage a vast number of queries and transactions simultaneously. This allows you to maintain system performance even during peak workloads.

High Performance for Deep Analytics

Graph databases deliver high-end performance for in-depth analytics, allowing you to derive hidden patterns and insights within your connected data points. This capability helps you perform advanced computations like shortest path analysis, community detection, or predictive modeling.

Types of Graph Databases

Graph databases can be categorized into two types—Property Graphs and RDF Graphs. Both types consist of nodes and edges, but they differ in functionality and use cases.

Property Graphs

If you need to analyze relationships in your data or perform sophisticated queries, property graphs are a good choice. A property graph includes nodes and edges with properties (key-value pairs) and can be referred to as a labeled property graph. Using Graph Query Language (GQL), you can query and extract information from these graphs.

Due to its versatility, you can use property graphs across various industries, including retail, finance, and manufacturing. They are well-suited for developing recommendation engines, fraud detection systems, or optimizing supply chain operations.

Here are a few examples of property graph databases:

• Neo4j: Neo4j is a single-model graph database focusing exclusively on property graphs, where nodes and edges can have properties as key-value pairs. It uses Cypher Query Language for property graph traversal and manipulation.‍
• TigerGraph: This is a distributed native graph-based database optimized for large-scale property graph operations. It supports GSQL, a graph query language developed for scalable analytics.‍
• ArangoDB: Unlike other types of NoSQL databases, ArangoDB is a popular multi-model database that supports property graphs, documents, and key-value data models. This unique capability helps you work with diverse data structures within a single platform using ArangoDB Query Language (AQL).

Resource Description Framework (RDF) Graphs

RDF Graphs follow W3C (World Wide Web Consortium) standards to help you manage complex metadata and master data. In the RDF model, data is represented in the form of statements, with each statement describing a relationship. These statements are expressed as triples comprising three components:

• Subject: It is a node that indicates the resource being described.‍
• Predicate: It defines the relationship between a subject and an object.‍
• Object: A node that specifies the resource related to the subject.

Each element in the statement is identified using a Uniform Resource Identifier (URI).

Consider an example:

‍Statement: Sam is a friend of George.

The RDF statement in triple structure:

Subject: Sam

Predicate: is a friend of

Object: George

The triple structure enables you to effectively depict relationships in a graph format. If you want to retrieve and manipulate RDF data, you can leverage a semantic query language known as SPARQL.

Let’s have a look at some examples of RDF graph databases:

• BlazeGraph: BlazeGraph is an ultra-high-performance, open-source graph database supporting the RDF data model. It utilizes GPU acceleration to enhance the effectiveness of graph traversal and query execution.‍
• Apache Jena: Jena is a Java-based framework that helps you to create and query RDF graphs. It provides APIs for parsing, manipulating, and querying RDF data using SPARQL.‍
• AllegroGraph: It is a persistent RDF database that facilitates SPARQL, full-text search, and geospatial-indexing capabilities. AllegroGraph utilizes disk-based storage to manage billions of triples while ensuring high-end performance.

NoSQL Graph Database Use Cases

Graph store NoSQL databases are beneficial for analyzing interrelated datasets and uncovering hidden connections. Here are two scenarios showing the potential of NoSQL graph databases:

Anti-Money Laundering

Money laundering has become difficult to track due to the rise of real-time digital payments, complicated international regulations, and the growth of cryptocurrencies. According to the United Nations Office on Drugs and Crime, between 2-5% of global GDP ($800 billion to $2 trillion) is laundered annually.

In response, governments have increased regulatory measures to combat money laundering. A study by Juniper Research predicts a 170% increase in global spending from non-financial institutions on anti-money laundering (AML) systems by 2028.

However, these AML systems depend on relational databases to manage data, including customer, account, and transaction information. While relational databases are useful for indexing, searching, and supporting basic transactions, they are ineffective for identifying hidden connections. They also struggle with analyzing complex money trails, which are key factors for assessing financial fraud risks.

This is where graph databases offer a solution to detect money laundering activities. You can construct a graph connecting entities, such as customers or accounts, and shared attributes, like email addresses. Once the graph is established, a simple query can help you identify all customers with similar account details. It also reveals the accounts involved in transferring funds to one another.

Supply Chain Planning

Jaguar Land Rover (JLR), a global automotive manufacturer, manages a highly complex supply chain due to the vast number of vehicle construction parts. The company experiences fluctuating customer preferences, manufacturing adjustments, and unexpected distortions, all of which can affect operations and profitability.

Conventionally, JLR relied on long-term sales forecasts to plan spare parts procurement. However, varying demands might lead to penalties for failing to meet minimum order requirements.

To address these challenges, JLR sought a solution capable of rapidly analyzing supply chain changes. Querying their data, which was spread across multiple systems, was previously impossible.

By adopting a graph-based NoSQL database solution like TigerGraph, JLR could combine data from 12 separate sources into a graph that is equivalent to 23 relational tables. This unified graph framework connects hundreds of supplier networks, model configurations, and manufacturing schedules. As a result, JLR can respond quickly to disruptions and make real-time decisions about vehicle production. According to Harry Powell, Director of Data & Analytics at JLR, utilizing a graph database significantly reduced supply chain processing time from days to mere hours.

Conclusion

Graph-based databases in NoSQL offer a powerful solution to help you handle complex relationships across several entities. By using its key features like relationship management, flexible data modeling, and advanced querying, you can derive deeper insights and make accurate decisions.

By understanding the capabilities of graph databases, you can fully utilize them to optimize existing apps or develop new solutions.