A Beginner’s Guide to Understanding Graph Database
Graph databases quickly become the forefront of scalable and flexible data storage and retrieval. Using graph theory to store information in a way that mimics how we remember things in our heads, there is no need for joins when retrieving sets of related data.
This guide will make you understand what is a graph database and why you need one.
What is a Graph Database?
According to reports, the global graph database market size was valued at $651 million in 2018.
A NoSQL database uses an associative array or collection of “nodes” and “edges” to store information. When input into the database, it creates an edge between two nodes with attributes attached.
These nodes are then connected by more edges, creating a web-like structure traversed. These structures are very similar to how we remember things in our heads.
For example, say you create an edge between two nodes: person and pet. One attribute might be the type of pet they have (cat). The next time you want to view their pets, you start with the person node, then follow the pet edge. It returns all of their pets in a list. You won’t be required to join any tables when retrieving this data because there is no need for an external source.
When using nodes and edges, graph databases are lightning-quick when querying large amounts of data. They’re also very good at storing relations, making them ideal for big data applications, such as fraud detection or master patient indexing.
Why Should You Use a Graph Database?
Once you know what is a graph database, you’ll want to know why you should use one. Here are reasons to use graph databases.
Better Data Retrieval
While relational databases are great for tabular data, this structure doesn’t work when you’re dealing with highly connected data. When your dataset is interconnected, graph databases have fewer joins and can return results faster.
Unlimited Data Growth
Because of their distributed nature, graph databases don’t limit the amount of data they can hold. You don’t have to manage. You are outgrowing your database with unlimited scaling.
Although they’re not relational, graph databases can scale horizontally, making them ideal for big data applications. They also allow you to continue working during these scaling operations.
Constant Data Growth
Another perk of graph databases is that they allow new information to be added without updating data models or refreshing materialized views. Because of this, applications can start using new data immediately, not after the dataset is refreshed.
Speed and Flexibility
Graph databases are lightning quick, making them ideal for real-time applications like fraud detection. They’re flexible as they can be used in various use cases and teams (and therefore technologies), such as Java and .NET. It means that you won’t have to start from scratch if you ever need to switch technologies.
Ease of Use
Because graph databases are so flexible and scalable, they’re easy to use. There is no need for complicated SQL queries or joins when retrieving data from a graph database, making them ideal for new developers.
Now that you know what a graph database is and why you need one, it’s time to try them. As more and more companies turn toward data-driven decision-making, your company needs to ensure its technology stack can keep up with these demands.