Solutions Review’s listing of the best Graph Databases is an annual mashup of products that best represent current market conditions, according to the crowd. Our editors selected the best Graph Databases based on each product’s Authority Score; a meta-analysis of real user sentiment through the web’s most trusted business software review sites and our own proprietary five-point inclusion criteria.
The editors at Solutions Review have developed this resource to assist buyers in search of the best Graph Databases to fit then needs of their organization. Choosing the right vendor and product can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best Graph Databases all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.
Note: Companies are listed in alphabetical order.
The Best Graph Databases
Description: The Cambridge Semantics AnzoGraph DB is a massively parallel processing graph database designed to hasten data integration analytics. The product includes more than 40 functions for regular line-of-business analytics along with views and windowed aggregates, as well as graph and data science algorithms to support in-graph feature engineering and transformations. It also enables application developers to build their own custom functions and aggregates that can perform in parallel across knowledge graphs.
Platform: DataStax Enterprise
Related products: DataStax Astra
Description: DataStax offers a distributed hybrid cloud database built on Apache Cassandra. The company’s flagship product is DataStax Enterprise, a solution that makes it easy for enterprise to exploit hybrid and multi-cloud environments via a data layer that eliminates complexity associated with deploying applications across multiple on-prem data centers or multiple public clouds. Its enterprise data layer eliminates data silos and cloud vendor lock-in and powers mission-critical applications.
Platform: MarkLogic Server
Related products: MarkLogic Data Hub, MarkLogic Data Hub Service
Description: MarkLogic has made a name for itself as a result of its strong focus on unifying silos of data. It is best for applications that involve heterogeneous large-scale data integration or content delivery. Organizations can ingest structured and unstructured data with a flexible data model that adapts to changing data. It also natively stores JSON, XML, text, and geospatial data. MarkLogic’s Universal Index enables users to search across all data, and APIs enable application development and deployment. The database has ACID transactions, scalability and elasticity, and certified security as well.
Platform: Neo4j Database
Related products: Neo4j Aura, Neo4j Desktop, Neo4j Bloom, Neo4j Graph Data Science Library
Description: Neo4j offers a graph database that helps organizations make sense of their data by revealing how people, processes and systems are related. Neo4j natively stores interconnected data so it’s easier to decipher data. The property graph model also makes it easier for organizations to evolve machine learning and AI models. The platform supports high-performance graph queries on large datasets as well.
Platform: OrientDB Enterprise
Description: OrientDB is a NoSQL database management system written in Java. It is a multi-model database that supports graph, document, key/value, and object models. Relationships are managed as in graph databases with direct connections between records. OrientDB development relies on an open source community that is led by OrientDB LTD, and uses GitHub to manage the source code, contributors and versioning. Google Group and Stack Overflow provide free support to users around the globe.
Platform: Redis Enterprise
Description: Redis Labs is best known for its Redis Enterprise, a database product that takes advantage of modern in-memory technologies like NVMe and Persistent Memory to provide deployment over cloud and on-prem data centers. The solution features native data structures and a variety of data modeling techniques such as streams, graph, document and machine learning with a real-time search engine. Redis has also had considerable success entering strategic partnerships with vendors such as Pivotal and Red Hat.
Platform: TigerGraph DB
Related products: TigerGraph Cloud
Description: TigerGraph offers a graph database platform for enterprise applications. The product supports real-time deep link analytics for organizations with large data volumes. TigerGraph can be utilized for applications like IoT, AI, and machine learning to make sense of changing big data. The solution also provides personalized recommendations, fraud prevention, supply-chain logistics, and company knowledge graph.