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 the 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: The best graph databases are listed in alphabetical order.
The Best Graph Databases
Platform: Amazon Neptune
Description: Amazon Neptune is a fully-managed graph database service that lets you build and run applications that work with highly connected datasets. The foundation for Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph quickly. It supports graph models like Property Graph and W3C’s RDF and their respective query languages Apache TinkerPop Gremlin and SPARQL as well. Neptune is recommended for use cases like fraud detection and network security.
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.
Description: Dgraph is a graph database solution that offers a single schema approach to development. The product lets users create a schema, deploy it, and gain fast database and API access without the need for code. Dgraph lets you choose from GraphQL or DQL so those with no prior experience using graph databases can get to work. The database also touts simple import or data streaming, as well as the ability to simplify business logic using Dgraph Lambda.
Platform: IBM Graph
Description: IBM Graph is an enterprise-grade property graph as a Service that is built on open-source database technologies. The product enables you to store, query, and visualize data points, connections, and properties in a property graph. IBM Graph was built to ensure always-on service while experts monitor, manage, and optimize everything in a customer’s stack. Organizations can start small and scale on-demand as data and complexity increase as well.
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: Azure Cosmos DB
Description: Azure Cosmos DB is a fully-managed NoSQL database service for modern application development. The product is also backed by SLAs, automatic and instant scalability, and open-source APIs for MongoDB and Cassandra, Users can run workloads with spiky or occasional traffic and moderate performance requirements with serverless, an alternative to provisioned throughput. Cosmos DB enables near real-time analytics and AI on operational data within existing SQL databases 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: Oracle Spatial and Graph
Description: Oracle Spatial and Graph is available as part of the company’s converged database offering. The Oracle Autonomous Database includes Graph Studio for one-click provisioning, integrated tooling, and security features. The product automates graph data management and simplifies modeling, analysis, and visualization across the entire lifecycle. Oracle provides support for both property and RDF knowledge graphs while interactive graph queries can run directly on graph data or in a high-performance memory graph.
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.