2018 is the year of graph databases. Here's why.

What are graph databases, anyway?

Simply put, graph databases are databases that follow the graph model. What is a graph model, then? In mathematical terms, a graph is simply a collection of nodes, with different nodes connected by edges. Each node contains some information about the graph, while edges denote the connection between the nodes.

How are graph databases different from the relational databases, you might ask? Well, the key difference between the two is the fact that graph data models allow for more flexible and fine-grained relationships between data objects, as compared to relational models. There are some more differences between the graph data model and the relational data model, which you should read through for more information.

Often, you will see that graph databases are without a schema. This allows for a very flexible data model, much like the document or key/value store database models. A unique feature of the graph databases, however, is that they also support relationships between the data objects like a relational database. This is useful because it allows for a more flexible and faster database, which can be invaluable to your project which demands a quicker response time.



Image courtesy DB-Engines

The rise in popularity of the graph database models over the last 5 years has been stunning, but not exactly surprising. If we were to drill down the 3 key factors that have propelled the popularity of graph databases to a whole new level, what would they be? Let’s find out.

Major players entering the graph database market

About a decade ago, the graph database family included just Neo4j and a couple of other less-popular graph databases. More recently, however, all the major players in the industry such as Oracle (Oracle Spatial and Graph), Microsoft (Graph Engine), SAP (SAP Hana as a graph store) and IBM (Compose for JanusGraph) have come up with graph offerings of their own. The most recent entrant to the graph database market is Amazon, with Amazon Neptune announced just last year.

According to Andy Jassy, CEO of Amazon Web Services, graph databases are becoming a part of the growing trend of multi-model databases. Per Jassy, these databases are finding increased adoption on the cloud as they support a myriad of useful data processing methods. The traditional over-reliance on relational databases is slowly breaking down, he says.

Rise of the Cypher Query Language

With graph databases slowly getting mainstream recognition and adoption, the major companies have identified the need for a standard query language for all graph databases. Similar to SQL, Cypher has emerged as a standard and is a widely-adopted alternative to write efficient and easy to understand graph queries.

As of today, the Cypher Query Language is used in popular graph databases such as Neo4j, SAP Hana, Redis graph and so on. The OpenCypher project, the project that develops and maintains Cypher, has also released Cypher for popular Big Data frameworks like Apache Spark.

Cypher’s popularity has risen tremendously over the last few years. The primary reason for this is the fact that like SQL, Cypher’s declarative nature allows users to state the actions they want performed on their graph data without explicitly specifying them.

Finding critical real-world applications

Graph databases were in the news as early as 2016, when the Panama paper leaks were revealed with the help of Neo4j and Linkurious, a data visualization software. In more recent times, graph databases have also found increased applications in online recommendation engines, as well as for performing tasks that include fraud detection and managing social media. Facebook’s search app also uses graph technology to map social relationships.

Graph databases are also finding applications in virtual assistants to drive conversations - eBay’s virtual shopping assistant is an example. Even NASA uses the knowledge graph architecture to find critical data.

What next for graph databases?

With growing adoption of graph databases, we expect graph-based platforms to soon become the foundational elements of many corporate tech stacks. The next focus area for these databases will be practical implementations such as graph analytics and building graph-based applications.

The rising number of graph databases would also mean more competition, and that is a good thing - competition will bring more innovation, and enable incorporation of more cutting-edge features. With a healthy and steadily growing community of developers, data scientists and even business analysts, this evolution may be on the cards, sooner than we might expect.

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