A Simple Introduction to Graph Database for Beginners and Why You Might Need it

All that to say: Graph technology is a rising tide for your development team. Graph databases are the future, and even if you’re just a beginner, it’s never too late to get started.

In this article, we will briefly take a look at what a graph database is and why it is important.

Introduction to Graph Database

In computing, a graph database (GDB) is a database that uses graph structures for semantic queries with nodesedges, and properties to represent and store data. A key concept of the system is the graph. The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation.

Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Graph databases are purpose-built to store and navigate relationships. What are relationships in a graph database, you might ask?

Relationships are first-class citizens in graph databases, and most of the value of graph databases is derived from these relationships. Graph databases use nodes to store data entities and edges to store relationships between entities. An edge always has a start node, end node, type, and direction, and an edge can describe parent-child relationships, actions, ownership, and the like. There is no limit to the number and kind of relationships a node can have. JSON LD and Schema.org are some of the examples of a graph database

How does Graph Database Actually Work?

Let us take a step back from the technical terms and understand the fundamentals of a graph database. Unlike other database management systems (DBMS), relationships take first priority in graph databases. In the graph world, connected data is equally (or more) important than individual data points.

This connections-first approach to data means relationships and connections are persisted through every part of the data lifecycle within a scalable, reliable database system. This is a very important concept to understand.

The result: Your data models are simpler yet more expressive than those you’d produce with relational databases or NoSQL (Not only SQL) stores. This also means your application doesn’t have to infer data connections using things like foreign keys or out-of-band processing.

Now, there are many ways the nodes of the graphs can be clustered and separated. Graphs can be clustered all together using algorithms that are effective and robust. One such algorithm is called hierarchical clustering.

Hierarchical clustering is a method that strategically organises related items into some sets of clusters. The final endpoint is a collection of groups, each different from the others, and the items inside each cluster are roughly similar to one another.

In some cases, the whole graphs database can be split into multiple nodes across a network in which everything is connected to one another. Machine learning can be applied to determine where the splits should happen so that there are no redundancies. Graphs can be used to generalize data in machine learning to determine features: You have a node of a person and there is a profession and now you can generalize the person to the profession.

What Makes Graph Database Unique?

There are 2 main points that makes a graph database unique. Let us take a look at them.

Graph Storage

Some graph databases use native graph storage that is specifically designed to store and manage graphs – from bare metal on up. Other graph technologies use relational, columnar or object-oriented databases as their storage layer. Non-native storage is often slower than a native approach because all of the graph connections have to be translated into a different data model.

Graph Processing

Graph processing is the most efficient means of processing because connected nodes physically point to each other in the database. Non-native graph processing engines use other means to process Create, Read, Update or Delete (CRUD) operations that aren’t optimized for handling connected data.

Types of Graph Database

Let us take a look at some of the different types of a graph database and some examples.


OrientDB is the first Multi-Model Open Source NoSQL DBMS that combines the power of graphs and the flexibility of documents into one scalable, high-performance operational database. OrientDB was engineered from the ground up with performance as a key specification. Below are some of the features of OrientDB.

  • Offers a better and more robust RAM usage
  • Relationships are physical links to the records, hence no more joints
  • Able to transverse different parts of or entire trees and graphs of records in milliseconds without the size of the records being an issue. Traverses parts of or entire trees and graphs of records in milliseconds


Neo4j is the world’s leading open-source Graph Database which is developed using Java technology. It is a highly scalable and schema-free (NoSQL) database.

  • Provides a flexible, simple and yet powerful data model, which can be easily changed according to the applications and industries
  • Spring Data Neo4j, part of the larger Spring Data family, provides easy configuration and access to Neo4j Graph Databases from Spring applications.
  • Connected and structured data can be easily represented
  • Provides a declarative query language to represent the graph visually, using an ASCII-art syntax. The commands of this language are in human-readable format and very easy to learn
  • It does not require complex joins to retrieve connected/related data as it is straightforward to retrieve its adjacent node or relationship details without joins or indexes
  • Neo4j supports full ACID (Atomicity, Consistency, Isolation, and Durability) rules


Gremlin is the graph traversal language of Apache TinkerPop. Gremlin is a functionaldata-flow language that enables users to succinctly express complex traversals on (or queries of) their application’s property graph.

  • Gremlin is an Apache Software Foundation query language and as such, can be used by any graph system (Neo4j, OrientDB).
  • It can be used on top of an existing graph database or a graph database cloud service. For instance, you can use gremlin on top of OrientDB. There is also some azure graph store where one can also use Gremlin.
  • Gremlin has a natural compilation of the common distributed vertex-centric computing model (bulk synchronous parallel for graphs). Thus, Gremlin works for both OLTP (graph databases) and OLAP (graph processors).
  • Gremlin can be embedded in any host language. The user’s database query code and data manipulation code are in the same language. There exists Gremlin-Java8, Gremlin-Groovy, Gremlin-Scala, Gremlin-Clojure, Gremlin-PHP, etc.
  • Gremlin supports both imperative path expressions and declarative pattern matching.

Multi Database Systems

In the real world environment, Big Data is not stored in a single database. Instead, the data is stored in multiple databases which can hold different types of data. This is called polyglot persistence in a more scientific manner.

It basically represents the data to be stored in multiple databases of different types and hybrids. It is chosen based upon the way data is being used by individual applications or components of a single application. This is because there is no single database that can fit all types of data.

Let us take a look at the example above. We can see that there are 4 different types of database that are used to store the data from the e-commerce platform. For example, the shopping cart and session data are stored in a key-value store database because it can be represented in a more simple manner where for each session data (key), you will have a shopping cart as the value.

From the graph store database perspective, since it focuses on creating and linking relationships between the data, a customer social implementation might be more suitable for this. Not to mention that mostly in e-commerce platforms, there are recommendations, so this might help as well. This cannot be achieved using a traditional database system as it would not represent the connections between all the data.

As each data storage solution is being implemented, it comes at a tradeoff to implement different technologies and software stack. But the benefits will be worth it.

Master Data using Graph Database

Master data is a comprehensive technique of enabling a business or an organisation to link all of its key data to a single file, known as a master file, which serves as a common point of reference (regardless of architectures, platforms, and applications).

Graphs describe data as well as the relationships between them. Networks and hierarchies are the most well-known types of graphs. Graphs have nodes and connections between them. Both nodes and connections can have attributes, which are categorised as labels and relationship types.

Building a master data network to manage internal organisation hierarchy data using a graph database is one example. Organizations may create a single graph model for widely dispersed data and then gather, store, and analyse this data on a graph database to gain a better understanding of their internal organisation linkages.

Connecting the dots

The real world is richly interconnected, and graph databases aim to mimic those sometimes consistent, sometimes-erratic relationships intuitively. That’s what makes the graph paradigm different from other database models: It maps more realistically to how the human brain maps and processes the world around it.

In this article, we understood the basics of a graph database and why it is essential to use it. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Graph databases are purpose-built to store and navigate relationships. There are also some popular graph databases including Gremlin, Neo4j and OrientDB that are more commonly used in the field.

In the real world environment, Big Data is not stored in a single database. Instead, the data is stored in multiple databases which can hold different types of data. This is called polyglot persistence in a more scientific manner.

In the next few articles of this series, we will dive deep into how these graph databases are actually constructed to handle different data types.

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