Why Graph Databases are a Must-Know for Today's Businesses: From Silos to Networks
As a former data analyst who's now taken the plunge into recruitment with my own agency, DR Analytics Recruitment, I've seen firsthand the importance of keeping one's finger on the pulse of the data world. One trend that's been growing in popularity is graph databases. In this article, I'll give you the lowdown on what they are and share three real-world applications.
Garnet forecasted that 80% of data and analytics innovations will be made using graph technology by 2025. Source
Now, what are Graph Databases?
Traditional relational databases have multiple tables that are often connected manually with SQL joins. Graph databases, on the other hand, use nodes and edges to represent entities and their relationships allowing multiple data points to be shown along with their accompanying relationship.
Graph vs. Relational Databases.
In a graph database, nodes represent entities (such as people, places, or things), while edges represent the relationships between them (such as friendships, distances, or ownership). This flexible structure allows for a more efficient and intuitive representation of complex data relationships, leading to better performance and scalability. Basically, forget about ever using a JOIN statement again.
The graph database ecosystem.
Real-World Applications of Graph Databases:
1. Fraud Detection in Banking and Finance
Financial institutions face the constant challenge of detecting and preventing fraudulent activities. Graph databases can help in identifying patterns and connections that may indicate fraudulent transactions or accounts. By analysing the relationships between accounts, transactions, and individuals, banks can quickly identify suspicious activities and take appropriate action. Relational databases often run into the problem of not being able to view all relationships across tables unless you use about 73 different join statements qhich is inefficient and time consuming.
2. Social Media Analysis and Recommendations
Social media platforms generate massive amounts of interconnected data, making them a perfect use case for graph databases. The best example of this is LinkedIn which is built on a graph database called Liquid. This is how they pull everyone's 1st, 2nd and 3rd connections without straining the system. By analysing the relationships between users, their interests, and their interactions, social media platforms can create highly personalised recommendations and advertisements, giving users more relevant content and connection suggestions.
3. Supply Chain Optimisation
Managing supply chains involves dealing with complex relationships between suppliers, manufacturers, distributors, and customers – a bit like organising a family reunion. Graph databases can help companies optimise their supply chains by providing a clearer understanding of these relationships and dependencies. By analysing the connections between various supply chain nodes, businesses can identify bottlenecks, optimise routes, and better predict and respond to disruptions.
There are data science, visualisation and analytics outcomes that come from graph!
Conclusion:
Graph databases are revolutionising the way we handle and analyse interconnected data, and it's not just a passing fad. From fraud detection in finance to personalised recommendations on social media platforms and supply chain optimisation, their real-world applications are transforming industries and creating new opportunities for data-driven insights. Graph is not going to replace relational databases anytime soon but there are certain use cases like fraud in banks where it may be applied on a project basis.
Some of the key players in the graph database landscape:
Neo4j: Popular graph database with native graph storage, processing, and Cypher query language.
Amazon Neptune: Fully managed graph database service by AWS, supporting property graph and RDF models.
ArangoDB: Multi-model NoSQL database supporting graph, document, and key-value data models with AQL query language.
OrientDB: Open-source, multi-model database with graph, document, key-value, and object-oriented data models, featuring a SQL-like query language.
JanusGraph: Scalable, open-source graph database optimized for large graphs, supporting various storage backends and indexing systems.
Lastly, LinkCurious is a GUI that sits on top of a graph solutions and allows for easy manipulation and use and MIP Australia provides consulting services in this domain!
LinCurious GUI
