When To Use Graph Databases: A Comprehensive Guide
In today’s digitized world, companies need to leverage dynamic and complex relationships in highly connected data to generate insight and competitive advantage. Whether it is about understanding relationships between entertainment producers and viewers, elements in a data center network, genes, and proteins, etc.,
The capability to analyze and understand vast connected data graphs will be the key to identifying organizations that are beating their competitors over the upcoming years. That’s where the Graph Database arises as it is the best way to represent and query any large volume of connected data.
Remember, connected data refers to those data sets whose value and interpretation require us to understand how their elements are related to each other. And to achieve that, the connections between these elements have to be named and defined.
Although in this blog we will cover graph databases and graph data models, we will not cover graph theory because we don’t need too much theory to leverage graph databases. But it is important to understand what a graph is, so let’s refresh our minds on what graphs are.
A graph is a collection of nodes and relationships that connects them, in mathematics, a set of vertices and edges. It represents entities as nodes and the way to relate those entities to the world as relationships. And this multipurpose, expressive structure of graphs enables us to model any kind of scenario, whether it is about constructing a road system, space rocket, or supply chain, and beyond.
Now that we know what a graph is, let’s move ahead with Graph Databases.
What is a Graph Database?
A graph database is an online database management system that uses graph structures i.e, nodes and relationships to store data instead of tables. With CRUD operations, including creating, reading, updating, and deleting, graph databases work on the graph data model.
A graph database prioritizes relationships between nodes so the application does not infer data connections via out-of-band processing or foreign keys. Generally, it is built for online transaction processing (OLTP) systems as Graph databases are optimized for performance with operational availability and transactional integrity.
A developer must consider the major properties of graph databases during searching technologies for it. These properties are:
The Processing System
The processing system in the graph database refers to how a graph database processes operations like storing and querying. Graph databases have index-free adjacency native processing capabilities that are the differentiator of processing native graphs.
A node whose adjacency is index-free refers directly to its nearby node and serves as a micro-index.
Making sure that every node is stored to its nodes and relationships adjacently speeds up the processing. During query processing, the index-free adjacency assures that the data is retrieved swiftly without relying on the indexes.
The Underlying Storage
Graph databases use underlying storage that is optimized for storing and managing graph data. If it is only designed to store graph data, it is called the native graph storage. Graph database native storage speeds up traversals during an arbitrary algorithm traversal of the graph database.
Here all the graph data stored in the files contain part of the graph such as relationships, nodes, properties, and labels. This division in the storage helps graph traversals to become highly performant. In the native graph storage, nodes aim to point out lists of labels, relationships, and properties.
What separates native storage from non-native graph storage is that non-native storage uses an object-oriented or relational database rather than being specially created for graph data uniqueness. Not being optimized for graphs, non-native storage can also cause problems in performance during data retrieval as all the nodes and relationships need to be reassembled for each query.
Conversely, native graph storage is designed from the ground up to handle highly interconnected datasets hence they are most effective for storing and retrieving graph data.
Advantages of Graph Database
Now that we know what a graph database is, let’s understand its advantages for a project your business might want to develop.
One of the reasons why graph databases have been so popularly adopted is the utter performance increase while working with connected data. As opposed to the relational database where the join-intensive performance of query decreases with expansion in the dataset, as queries are localized in a graph portion, the performance of the graph database does not change with the size of the dataset.
Graph database API and query language allow controlled applications to be developed due to their schema-free nature and testability. It also allows developers to make changes according to the business requirements with graceful system maintenance.
Naturally, graphs are additive, i.e., they enable developers to create new relationships, labels, nodes, and subgraphs to the already existing structure without any friction. Moreover, due to the flexible nature of graphs, there’s no need to model the domain in tiring before developers are required to do so.
When To Use Graph Databases?
There are several use cases for Graph databases. Below are some of them:
Read: Top NoSQL Databases
GDPR and Regulation Compliances
When To Use Graph Databases? With graphs making it easier to manage and track data movements and security along with databases to reduce data breaches, using graph databases provides better data removing consistency while enhancing overall trust with private or sensitive data.
Master Data Management
As data management is essential to enterprise-level businesses, graph databases help companies to link all of their data at one location of reference which increases the accuracy and consistency of data.
Real-Time Recommendation Engine
For companies like eBay, Netflix, and Walmart to offer more accurate and valuable recommendations to their clients, graph databases can prove to be very helpful and profitable.
If your project needs a database that can provide a logical solution to real-life problems like traffic updates, or disaster warnings, graph databases can be highly beneficial for you.
Digital asset management
With a continuously growing amount of digital content, it becomes increasingly important to keep track of digital assets like contracts, evaluations, etc. In this scenario, graph databases can easily offer scalable, straight database models to your business.
Processing natural language into machine language and vice versa are ambiguous. Hence, the semantic search feature of graph databases can help in offering meaning behind to offer relevant results and makes it easy to map them.
If you want to identify fraudulent activities, transactions, or suspicious patterns, graph databases can offer real-time detection of deceptive behavior.
Graph databases are the perfect choice for routing information through networks because they find optimal paths.
In essence, networks are linked graphs. Using graph databases saves time for network administrators who need to be alerted about network problems.
So that's it for the blog. But before ending it, here are a few takeaways that we have acquired from here:
Highly connected data can be stored and queried using graph databases, which can offer high query performance.
When data is highly connected across multiple domains, it is useful - but beware of cases when this isn't the case, or when bulk queries are required with unknown starting points. For undifferentiated bulk storage or disconnected data, graphs are not an effective format.
The techniques of graph theory and GDS complement each other. But using graphs does not require you to understand the theory.
Besides, some of the graph databases that you can use are Neo4j, Amazon Neptune, DataStax, ArangoDB, JanusGraph, etc.
And as a developer, you need to start working with a lot of databases, and the Graph database is one of the most important ones.