Graph databases store information as nodes and data specifying their relationships with other nodes. They are proven architectures for storing data with complex relationships.
Their usage has grown during the past decade, despite companies considering other NoSQL and big data technologies.
The global graph database market was estimated at $651 million in 2018 and is forecasted to grow to $3.73 billion by 2026.
Competitors remain in the range of other big data management technologies, including Hadoop and Spark. And competition grew in popularity, skill adoption, and production use cases more than graph databases.
Graph databases and query languages
Developers think in objects and use hierarchical data representations in XML and JSON regularly.
For graph databases, although it may be relatively easy to comprehend the modeling of nodes and relationships used, querying them requires learning new practices and skills.
Developers can query Neo4j graph databases using Resource Description Framework (RDF) and Gremlin, but 90% prefer to use Cypher.
The query is elegant and efficient but has a learning curve for those used to writing SQL queries. Here’s one of the first challenges for organizations moving toward graph databases: SQL is a pervasive skill set, and Cypher and other graph query languages are a new skill to learn.
These databases can be used in flexible hierarchy design
Product catalogs, content management systems, project management applications, ERPs and CRMs all use hierarchies to categorize and tag information. Graph databases enable arbitrary hierarchies and developers to create different views of the hierarchy for different needs.
To take advantage of flexible hierarchies, it helps to design applications from the ground up with a graph database. The entire application is then designed based on querying the graph and leveraging the nodes, relationships, labels, and properties of the graph.
Databases and cloud deployment – reduced operational complexity
Deploying data management solutions into a data center must consider infrastructure and operations, security requirements and review performance considerations. These are used to size up servers, storage, and networks, as well as replicated systems for redundancy and disaster recovery.
Organizations experimenting with graph databases now have several cloud options. Engineers can deploy Neo4j to GCP, AWS, Azure, or leverage Neo4j’s Aura, a database as a service.
The public cloud vendors have graph database capabilities. These include AWS Neptune, the Gremlin API in Azure’s CosmoDB, the open source JanusGraph on GCP, or the graph features in Oracle’s Cloud Database Services.