Following the success of NoSQL models, multi-model databases emerged as an answer to the complexity that the multiplication of siloed systems was creating. These databases are designed to support various data types, handling in one single data store various models such as document, key-value, RDF and graphs. They are particularly convenient if you need to work with multiple data types but want to avoid the operational complexity of managing various silos.
Among the native multi-model databases that include graph as a supported model, we can name ArangoDB. This open-source multi-model database was released in 2011 and supports three data models: key/value, documents and graphs. Cosmos DB is Microsoft Azure’s latest addition to the multi-model landscape. Launched in 2017, this distributed cloud database supports four data types: key-value, document, column family and graph. DataStax Enterprise is also a distributed cloud database, built on top of the open source NoSQL Apache Cassandra system. The system supports column family, documents, key-value, and graph since the addition of DataStax Enterprise Graph in 2016. Finally MarkLogic is a historical stakeholder who added RDF triples support to its existing supported document model back in 2013.
Another strong signal of market traction was the evolution of the database main players’ strategy. Over the last few years, we saw traditional relational store heavyweights add graph capabilities to their systems through dedicated APIs. In 2012, IBM added a NoSQL graph store, DB2-RDF to its database. One year later, Oracle rebranded its database graph option to Oracle Spatial and Graph, known today as Oracle Big Data Spatial and Graph. More recently in 2016, SAP Hana announced the released of SAP HANA Graph, expanding the capabilities of its relational DBMS with support for graphs.