Data is everywhere. It surrounds us and encapsulates our lives. As humans living in a modern world, we generate tons of data every single day. But data is just data if it’s never connected. The value of data resides in the relationships between data points. In the last two articles, we discussed the concept of connected data and the benefits of graph databases. We’ve solved the conundrum presented by the title of this article with the simplest of answers, a graph database.
What if I told you we could take that a step further? Take all this data that is connected and drive even more value out of it with knowledge graphs. You’re probably wondering but aren’t they the same things?
Well no. Think of knowledge graphs as the upgrade to the standard graph. When you add relationships to data, you get graphs but add semantics, and you get a knowledge graph that can provide your organisation with a dataset that accommodates deep dynamic context to ensure you make the best-informed decisions.
The question now is, how do you get from graph to knowledge graph? With Neo4j, of course, it’s easy, the native graph database that prioritises meaningful data connections.
Getting Started With Neo4j
But first, to create a knowledge graph, the adoption of a graph database is essential. Neo4j is a leading native graph database optimised for dealing with graph data from the ground up. Like traditional relational databases, Neo4j prides itself on being ACID (Atomicity, Consistency, Isolation, Durability) compliant, giving organisations the good qualities of a relational database with a different data model.
Cypher is what sets Neo4j apart from other graph databases today. It is their declarative query language that makes the database understandable and workable for every database user. Unlike an imperative query language, Cypher allows you to state what you are looking for and let the database worry about retrieving that data.
The underlying data model for Neo4j is the labelled property graph, chosen for its versatility and flexibility to deal with real-world datasets. This model is based on four fundamental building blocks that help store and structure data. Nodes are used to store entity information, while relationships are used to connect nodes. Both nodes and relationships are containers for properties, while labels are a means to create subgraphs efficiently. There is no longer a need for complex join tables or to wait for time-consuming queries using the graph model.
Without data, databases are essentially useless. Having understood the importance of a graph database, it’s now time to import data into your Neo4j graph database. But every import is different, and understanding that can help you choose the right toolset for a successful import. Neo4j offers many options to fit the needs of different datasets. Whether it be using spreadsheets, Neo4j-shell-tools or Load CSV, there will be pros and cons that fall on the shoulder of the developers. But what you can be sure of is the effort will be well worth it in the end.
Although importing data can be a nuisance, many customisable tools can be used to import data into Neo4j to begin leveraging your connected data.
In an age where attention spans barely reach a minute, data analysis can no longer depend on the cognitive approach. Today’s workers demand the ability to analyse data at a glance; therefore, visualisation is an integral part of the graph database approach.
To cater to the virtualisation needs of the varied uses of the graph database, Neo4j’s graph visualisation tools fall into three architectural categories. Developers can choose between embeddable tools with built-in Neo4j connections, embeddable libraries without direct Neo4j connections and standalone product tools. One such standalone tool is Bloom by Neo4j, which allows users to explore data without the need for query language or programming.
Neo4j gives an in-depth look into the details of achieving graph productivity in this e-book for developers looking to step into the world of graph databases. What are you waiting for? You have the steps to leverage knowledge graphs. Explore what Neo4j has to offer now!