Graph Databases Not All Hype
At least that’s what I thought when I first starting hearing about graph database technology. I’m a business person, not a database expert. Our business is simply stated, delivering high value insights for the enterprise through analytics. So, when our CTO, Kurt Rosenfeld, explained to me what “what is possible” with graph databases I knew I should take a closer look.
This is a case where the reality might actually live up to the hype.
Today’s CIOs and CTOs don’t just need to manage larger volumes of data – they need to generate insight from their existing data to deliver value back to the business. Relationships are as important as the data itself in today’s connected world. Use cases that require modeling complex relationships are the best for graph databases. In order to leverage data relationships, organizations need a database technology that stores relationship information as a first-class entity. That technology is a graph database.
As they say, a picture is worth 1000 words… Neo4j, the creators of the graph platform by that name, has a simple depiction of how persistent relationships that can be established:
Graph databases are optimized for managing highly related data and complex queries. They enable focus on the relationships rather than the data itself. A graph database stores persistent direct links that can be queried efficiently, and response times are independent of the total size of the dataset.
Graph databases perform better than relational (SQL) and non-relational (NoSQL) databases. The key is that, as data queries increase exponentially, the performance of the graph database does not deteriorate, compared to what happens with relational databases.
Graph can be used by any size or type of organization, whether as a central database platform or alongside an existing database. Some of the common use cases and solutions we’ve seen are:
- Fraud Detection – first-party bank fraud, credit card fraud, eCommerce fraud, insurance fraud and money.
- Network & IT Operations – gain insights into the complex relationships between different operations, from dependency management to automated micro-service monitoring.
- Real-Time Recommendation Engines – correlate product, customer, inventory, supplier, logistics and even social sentiment data. Instantly capture any new interests shown in the customer’s’ current visit.
- Master Data Management – unify your master data, including customer, product, supplier and logistics information to power the next generation of eCommerce, fraud detection, supply chain and logistics applications
- Identity & Access Management – Managing multiple changing roles, groups, products and authorizations seamlessly track all identity and access authorizations and inheritances
By advancing the case for real-time analytics along with the capacity for delving into highly complex data relationships, graph databases are raising overall corporate awareness about the importance of data analytics to drive to a business outcome. Being able to quickly identify complex relationships and meanings of data from many different sources(which, of course, is what big data is all about) is paramount. Adopting graph database technology can give a competitive advantage to data-driven organizations.
Any organization now has the ability to exploit relationships between different types of data – in real time at scale, without the inherit limitations of current database technologies thanks to graph. More and more businesses today have adopted the core belief that connections between data are as vital as the data itself.
So, does graph live up to the hype? Only if you want to stay ahead of your competition.