Graph Database: How It Works, Its Uses & Benefits

• Read original article here

InfluxData is a remote-first company that’s growing rapidly worldwide. Join us!
A Guide to Graph Databases
Graph Databases: How They Work, When to Use Them & the Advantages They Offer
In many of your business dealings, you see natural data relationships that evolve. In such a blended and interconnected world, it’s normal to see the evolution of data as something dynamic and constantly changing while also integrating networks of people and relationships between them. To support their communities and offer the best possible user experience, companies now need to track relationships between millions of people interacting in a myriad of divergent ways.
Financial services companies track behaviors and money flows across their account holders to upsell their services, detect fraud and prevent loss. Project managers track the interrelationships between vendors and timelines to plan and implement their project goals. Virtually every industry has some form of interaction or interrelationship that benefits from tracking flows of data and resources across various channels in an interconnected framework.
In this article you will learn how graph databases can be used to simplify handling these relationships between data while also making it easier for developers and data analysts to use that data to drive business decisions.
Graph database FAQ
What is a graph database?
A graph database is a specialized NoSQL database designed for storing and querying data that is connected via defined relationships. Data points in a graph database are called nodes and these nodes are connected to related data via edges. The data attached to each node are known as properties. Graph databases aren’t restricted by predefined schema like relational databases, and this flexibility allows for data to be connected naturally through the life of an application.
Because of their simplicity and ease of use, graph databases are quickly becoming one of the fastest-growing categories in data management.
Graph database use cases
Developers and analysts use graph databases for a range of use-case scenarios. As you use relationships to process the transactions in your graph databases, you can detect scenarios where a single purchase relates to other data related to a customer, products, regional data, and other data.
Fraud detection
With a graph database, you can process purchase and financial transactions in (almost) real-time, which means you can prevent fraud. With a graph database, you can easily detect if a certain email address and credit card are related to other fraudulent charges.
With fraud detection, you can also differentiate accounts where a single email address is being used for multiple people. You can find scenarios where various people are associated with a single IP address, even though they have multiple physical addresses in different accounts.
Master data management (MDM)
Master data management (MDM) is a record of everything that’s essential about your company’s operations. It could include everything about accounts, business units, customers, locations, partners, products, and users. With a graph database, you can connect all that master data to solve pressing business questions. With its immediate business value, you can gain a competitive advantage as you’re able to manage your connected data better and understand your networks.
Network and IT operations
You can easily connect your monitoring tools across your network and IT operations with a graph database. Not only can you gain valuable performance insights, but you can better gauge vulnerabilities, troubleshoot solutions, conduct capacity planning, and better prepare your organization with impact analysis based on user guides.
Identity and access management (IAM)
You can identify and manage changing authorizations, groups, roles, and products with a graph database. As these interrelationships become ever-more complex, you can track all the data and better control access for your native graph with real-time results. With the interconnected nature of a graph database, you can support an intuitive access management relationship. You can be faster and more accurate while ensuring a greater level of efficiency across your organization.
Recommendation engines
You can easily store a customer’s friends, interests, and purchase history with a graph database. Based on your analysis of what you can see about the relationships between these variables, you can offer a recommendation engine that will offer ideas of what the user will like and prefer. For example, you can extrapolate with a high degree of accuracy that a customer might like products like those another user bought if/when they have the same purchasing history and behavior.
Why should you use a graph database?
A graph database allows you to quickly and easily store data and analyze the relationships among data, so you can better understand the myriad of possible outcomes.
Graphs are everywhere
The most obvious example of a graph database is a social network, but you can see them in business transactions, recommendations based on connections, routing, and the logistics involved in optimal paths related to things like supply chain management.
Support simple modeling
With a graph database, you model based on understanding the problem, so it’s much cleaner and more simplified. It’s an easy-to-understand model which you can use to represent and store complex data.
Use structured or unstructured data
With a graph database, you can support a range of data demands with structured, non structured, and even a hybrid solution to meet your needs.
Simple querying
While almost any graph query could be performed on a relational database using SQL, the query would be extremely complex. Most graph databases have query languages built around the idea of working with edges and nodes and traversing a graph structure. The result is simpler queries that are faster to write and easier to understand.
Here’s an example showing the difference in query complexity between standard SQL versus the Cypher query language used with the Neo4J graph database. The query is grabbing a territory description by using the name of a sales rep employed by the company.
SQL:
SELECT e.LastName, et.Description FROM Employee AS e JOIN EmployeeTerritory AS et ON (et.EmployeeID = e.EmployeeID) JOIN Territory AS t ON (et.TerritoryID = t.TerritoryID);
Cypher:
MATCH (t:Territory)