Graph Databases Seen ‘Connecting the Dots’
Graph databases are increasingly seen as a way to “connect the dots” as enterprise applications emerge requiring the linking of huge datasets.
In an overview of the emerging graph database market, Forrester Research Inc. forecasts significant growth in the number of vendors and open source projects as demand grows for tighter linkages between data and customers. The promise of merging large datasets could have direct applications for cyber-security, social network analysis and predictive analytics, the researcher found.
The report identifies eight leading graph database vendors, the majority of whom are focused on “property” graphs used primarily for recommendation engines and real-time recommendations, social networking along with fraud detection. Leading vendors include DataStax, Neo Technology and Orient Technologies, Forrester Research reckons.
The other leading graph database type is called RDF triples stores. Rather than storing data as a graph model, this approach focuses instead on delivering improved graph query capabilities. Leading vendors, according to Forrester, include Complexible and Franz.
Other key players in the graph database market include FlockDB, Objectivity and Oracle. All three focus primarily on the property graph model.
The study also found a healthy mix of commercial and open source development projects and graph database platforms. Open source efforts include Apache Giraph, ArangoDB, FlockDB and Sparksee. Most are expected to bear fruit as broader uses cases emerge and, with them, commercial support.
Forrester foresees greater investment in graph databases as businesses leverage the ability to connect larger datasets to find new use cases ranging from gauging customer preferences to detecting fraudulent insurance claims. “A graph database allows organizations to think differently and create new intelligence-based business opportunities that weren’t possible before,” the Forrester survey asserts.
Graph database technology is seen as the next big leap in the analytics technology given its ability to handle more and larger datasets while making more meaningful connections between data. Hence, its potential big data use cases are seen as leapfrogging traditional analytics provided by relational databases, key-value stores and the current favorite, Hadoop and its various distributions.
Along with big data and real-time analytics, fraud detection and social networking use cases, the Forrester survey identifies “master data management” as an emerging application for graph databases. For example, a graph database “puts master data into a more intuitive, intelligent and integrated context,” the market researcher concluded, enabling a clearer view of customer preferences or product performance.
Graph database technology is also touted in the survey for delivering a “360-degree view” of a business by enabling companies to extract, store and analyze data derived from social networks, packages applications and on-premise databases, Forrester said.
As data volumes continue to explode, “It’s all about connected data, linking one element to another and then another, to drive insights,” Forrester Research concludes. Hence, the growing number of graph database vendors and open source projects may be in the best position to make sense of “connected data.”