Best Graph Databases of 2024

Cayley is an open source database for Linked Data. It was inspired by Google's Knowledge Graph graph database (formerly Freebase). Cayley is an open source graph database that allows you to store complex data and makes it easy to use. Built-in query editor, visualizer, and REPL. Cayley supports multiple query languages, including Gizmo, a query engine inspired by Gremlin and GraphQL-inspired query languages, MQL, a simplified version for Freebase lovers, and MQL. Cayley is modular and easy to connect with your favorite programming languages. It can also be used by back-end stores. Cayley has been well tested and used by many companies for their production workloads. It is also fast and optimized for use in applications. Rough performance testing has shown that on 2014 consumer hardware, 134m quads of LevelDB are not a problem, and a multi-hop intersection query - films starring X or Y - takes 150ms. Cayley is set up to run in memory by default (that's what backendmemstore means).

Realtime, realtime, offline-first, graph database engine. You can store, load, and share the data you need in your app without worrying too much about servers, network calls, database access, or tracking offline changes. GUN is a simple, fast, and easy-to-use data sync and storage tool that runs wherever JavaScript does. GUN's goal is to let you concentrate on the data that must be stored, loaded, shared, and shared in your app. It doesn't need to worry about servers, database calls, tracking offline changes, concurrency conflicts, or monitoring network calls. This allows you to quickly build cool apps. GUN gives you the most powerful tools of the internet, decentralization and privacy. This allows you to reclaim the web and make the internet truly open and free. GUN is a database engine which runs on all JavaScript devices, including mobile devices and servers. It allows you to create the data system that you want.

Blazegraph™, a graph database that supports Blueprints and RDF/SPARQL, is an ultra-high-performance graph database. It can support up to 50 Billion edges per machine. It is currently in production for Fortune 500 customers like EMC, Autodesk, among others. It supports key Precision Medicine applications, and is widely used for life sciences applications. It is extensively used to support Cyber analytics in government and commercial applications. It powers Wikidata Query Service, a Wikimedia Foundation project. You can choose an executable jar, war file, or tar.gz distribution. Blazegraph was designed to be simple to use and easy to get started. This is why it ships without SSL and authentication by default. We strongly recommend that you enable SSL, authentication and the appropriate network configurations for production deployments. Below are some useful links to help you do this.

Apache Giraph, an iterative graph processing platform, is designed for high scalability. It is currently used by Facebook to analyze the social network formed by users and their relationships. Giraph was originally developed as an open-source alternative to Pregel, a graph processing architecture that Google created and was described in a 2010 paper. Both systems are inspired from the Bulk Synchronous Parallel model for distributed computation, which Leslie Valiant introduced. Giraph has many additional features to the Pregel model. These include master computation, sharded aggregaters, edge-oriented in, out-of core computation, and more. Giraph has a steady development cycle, a growing user base worldwide, and is an ideal choice to unleash the potential of structured data on a large scale. Apache Giraph is an iterative graph processing framework built on Apache Hadoop.

The database is the foundation of intelligent systems. Grakn is an intelligent database, a knowledge graph. A data schema that is intuitive and expressive. It can be used to create rich knowledge models by defining hierarchies, hyperentities, hyperrelations, rules, and constructs. Intelligent language that infers data types, relationships and attributes, as well as complex patterns, at runtime and with persistent and distributed data. Accessible through simple queries, out-of-the box distributed analytics (Pregel & MapReduce), are available through the language. Strong abstraction allows for simpler expressions of complex constructs while the system determines the best query execution. Grakn KGMS & Workbase allow you to scale your enterprise Knowledge Graph. A distributed database that can scale across a network of computers by partitioning and replicating.

You can create high-quality, mission-critical applications in a fraction of the time and costs. Memstate is new. It is extremely inefficient to move data between RAM and disk. Additionally, it requires complex software that can be eliminated. Memstate allows you to manage and structure your data in-memory. It also provides transparent persistence, concurrency control, and transactions with strong ACID warranties. This is too technical. Make your applications faster and your developers more productive. Memstate can be used in many ways, but it is designed to handle complex OLTP workloads within an enterprise application. In-memory operations are a factor of ten times faster than disk operations. A single Memstate engine can execute millions upon millions of read transactions, and tens to thousands upon thousands of write transactions per second. All this at submillisecond latency.

HyperGraphDB is an open-source, general-purpose data storage system that uses a powerful knowledge management approach called directed hypergraphs. Although it is a persistent memory model, it can also serve as an embedded object-oriented data base for Java projects of any size. Or a graph database or a (non SQLL) relational database. HyperGraphDB is a storage system that uses generalized hypergraphs for its underlying data model. A tuple is a collection of 0 or more tuples. Each atom is a tuple of this type. The data model can be viewed as either relational, where higher-order, non-ary relationships are permitted, or graph-oriented where edges point to an arbitrary set nodes. Each atom is assigned a strongly-typed, arbitrary value. The hypergraph that manages these values is embedded in the type system and can be customized from the ground up.

Companies who choose a DIY approach to their graph database can expect a wait of 2 to 3 months before production-ready implementation. Your production-ready database will be available within minutes with Graph Story's managed services. Learn more about graph use cases and compare self-hosting to managed services. We can deploy your servers where they are already located: AWS, Azure or Google Compute Engine in any region. Do you need VPC peering? Let us know. We are flexible like that. How do you build a proof-of-concept? In just a few clicks, you can fire up one enterprise graph instance. Do you need to move to a cluster that is high-availability and production-ready on-demand? We've got you covered! We created graph db management tools to make it easy for you! You can see CPU, Memory, and Disk utilization in one glance. Access configs, logs and backups of your database.

HugeGraph is a high-speed, highly-scalable graph database. HugeGraph's excellent OLTP capability allows for the storage and querying of billions of edges and vertices. Gremlin, a powerful graph traversal and query language, can handle complex graph queries in compliance with Apache TinkerPop 3. It supports Gremlin and is compliant to Apache TinkerPop 3. Schema Metadata Management includes VertexLabel EdgeLabel PropertyKey and IndexLabel. Multi-type Indexes that support complex combination queries, range query, and exact query. Plug-in Backend Store Driver Framework. Supports RocksDB, Cassandra and ScyllaDB. It is easy to add another backend store driver if necessary. Integration with Hadoop/Spark. HugeGraph is built on the TinkerPop framework. We refer to the storage structure and schema definition of DataStax.

Graph databases are part of Oracle's converged data platform. They eliminate the need for a separate database to store and move data. Analysts and developers are able to detect fraud in banking, locate connections and link data, and improve traceability and smart manufacturing traceability. All this while gaining enterprise-grade security and ease of data ingestion and strong support for data workloads. Oracle Autonomous Database also includes Graph Studio. It offers one-click provisioning, integrated tools, and security. Graph Studio automates graph data administration and simplifies analysis, modeling, and visualization throughout the graph analytics lifecycle. Oracle supports both RDF knowledge graphs and property graphs. It also simplifies the process for modeling relational data as graph structures. Interactive graph queries can be run directly on graph data, or in high-performance, in-memory graph servers.

The Open, Multi-Cloud Stack to Modern Data Apps. Built on Apache Cassandra™, an open-source Apache Cassandra™. Global scale and 100% uptime without vendor lock in You can deploy on multi-clouds, open-source, on-prem and Kubernetes. For a lower TCO, use elastic and pay-as you-go. Stargate APIs allow you to build faster with NoSQL, reactive, JSON and REST. Avoid the complexity of multiple OSS projects or APIs that don’t scale. It is ideal for commerce, mobile and AI/ML. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Richly interactive apps that are viral-ready and elastic using REST, GraphQL and JSON. Pay-as you-go Apache Cassandra DBaaS which scales easily and affordably

Dgraph is an open-source, low-latency, high throughput native and distributed graph database. DGraph is designed to scale easily to meet the needs for small startups and large companies with huge amounts of data. It can handle terabytes structured data on commodity hardware with low latency to respond to user queries. It addresses business needs and can be used in cases that involve diverse social and knowledge networks, real-time recommendation engines and semantic search, pattern matching, fraud detection, serving relationship information, and serving web applications.

JanusGraph is an optimized graph database that can store and query graphs with hundreds of billions of edges and vertices distributed across a multi-machine cluster. JanusGraph is a project of The Linux Foundation and includes participants from Expero and Google, GRAKN.AI., Hortonworks. IBM, and Amazon. Linear and elastic scaling for growing data and users. Data replication and data distribution for performance and fault tolerance. Hot backups and high availability for multi-datacenters All functionality is completely free. There is no need to purchase commercial licenses. JanusGraph is completely open source under the Apache 2 License. JanusGraph is an open source transactional database that can handle thousands of concurrent users performing complex graph traversals in real-time. ACID and eventual consistency support. JanusGraph offers online transactional processing (OLTP) and global graph analytics (OLAP), through its Apache Spark integration.

XtendR unhides detailed, privacy-preserving insights across multiple independent data sources. XtendR allows you to access previously unaccessible data and protects your data throughout its lifecycle. This gives you complete privacy and regulatory compliance. XtendR provides more than anonymity. It's the missing piece in multi-party data sharing that offers true privacy protection. It is cryptography on duty to help you reach your full potential. The most advanced privacy-enhancing technology for data collaboration. Xtendr solved decades-old cryptography challenges of data sharing among mutually distrusting parties. Enterprise-grade data protection allows you to expand your business by allowing individual organizations to form data partnerships and protecting sensitive data. Data is the currency in our digital age. Some argue that data is replacing oil as the most valuable resource in the world, and there is no doubt about how important it is.

The graph database is designed for graphs up to super large scale with very low latency. We continue to work with the community to promote, popularize, and prepare the graph database. Nebula Graph allows only authenticated access through role-based access control. Nebula Graph can support multiple storage engines and the query language is extensible to support new algorithms. Nebula Graph offers low latency read/write while maintaining high throughput to simplify complex data sets. Nebula Graph's distributed, shared-nothing architecture allows for linear scaling. Nebula Graph's SQL query language is similar to SQL and can be used to address complex business requirements. Nebula Graph's horizontal scalability, snapshot feature and high availability guarantee that there will be no downtime. Nebula Graph has been used in production environments by large Internet companies such as JD, Meituan and Xiaohongshu.

GraphBase (Graph Database Management System, Graph DBMS), is a Graph Database Management System designed to simplify the creation and maintenance complex data graphs. The Relational Database Management System is challenged by complex and interconnected structures. A graph database offers better modeling utility, performance, and scalability. The triplestores and property diagrams are the most recent graph database products. They have been around for almost two decades. Although they are powerful tools with many uses, they are not well-suited for managing complex data structures. GraphBase was created to make complex data management easier. It could be Knowledge. This was possible by redefining the way graph data should be managed. GraphBase makes the graph a first-class citizen. A graph equivalent to the "rows & tables" paradigm makes it so easy to use a Relational Database.

Graph Engine (GE), a distributed in-memory processing engine, is supported by a strongly-typed RAM storage and a general distributed computing engine. The distributed RAM store is a global addressable, high-performance key-value storage that can be accessed by a cluster of computers. GE's RAM store allows fast random data access over a large data set. GE is a natural platform for large graph processing due to its ability to speed data exploration and distribute parallel computing. GE supports both low latency online query processing as well as high-throughput offline analysis on billion-node large Graphs. Schema is important when data processing must be efficient. For data storage that is compact, quick and clear, strong data modeling is essential. GE has the ability to manage billions of runtime objects of different sizes. As the number of objects increases, each byte counts. GE offers fast memory reallocation and allocation with high memory ratios.

Powerful graph analytics made easy. Aster SQL-GR™, a native graph processing engine for graph analysis, makes it easy to solve complex business issues such as social network/influencer analysis. It also helps with fraud detection, supply chain management and network analysis. These problems are more impactful than simple graph navigation analysis. SQL-GR is based upon the Bulk Synchronous Process (BSP) model. It uses massively iterative and parallel processing to solve complex graph problems. SQL-GR is extremely scalable because it is based upon the BSP iterative process model. It also takes advantage of Teradata Aster’s massively scalable parallel processor (MPP) architecture to distribute graph processing across multiple servers/nodes. SQL-GR does not have memory limits and is not limited to one server/node. SQL-GR can easily perform complex graph analysis on large data sets with unmatched speed and power.

AnzoGraph DB offers a wide range of analytical features that can be used to enhance your analytical framework. This video will show you how AnzoGraph DB, a native graph database for massively parallel processing (MPP), is designed for data harmonization. Horizontally scalable graph database designed for online analytics and harmonization. AnzoGraphDB, a market-leading graph database, can help you tackle linked data problems and data harmonization. AnzoGraph DB offers industrialized online performance for enterprise-scale graph apps. AnzoGraph DB supports Labeled Property Graphs (LPGs) and familiar SPARQL*/OWL semantic graphs. You have access to many data science, machine learning, and analytical capabilities that will help you gain new insights at an unparalleled speed and scale. Your analysis will be more effective if you consider the context and relationships of data. Data loading and queries ultra-fast

Sparksee, formerly known as DEX, is space- and performance-friendly. It has a small footprint and can quickly analyze large networks. It is natively compatible with.Net, C++ and Python and Objective-C. The graph is represented using bitmap data structures, which allow for high compression rates. Each bitmap is divided into chunks that can be placed on disk pages to increase I/O location. Bitmaps allow operations to be computed using binary logic instructions, which simplify execution in pipelined processors. Full native indexing allows for extremely fast access to all graph data structures. Bitmaps are used to represent node adjacencies in order to reduce their footprint. Advanced I/O policies reduce the number of times each page is brought into memory. Each value in the database can only be represented once, which prevents unnecessary replication.

Data collaboration made easy. We make collaboration easy for developers looking to innovate and data people looking for version control. TerminusDB is an open source knowledge graph database that allows for reliable, private and efficient revision control and collaboration. Nothing will make it easier to collaborate with others or create data-intensive apps. TerminusDB offers a full range of revision control features. TerminusHub allows users access to databases and to collaborate on shared resources. Flexible data storage, versioning, and sharing capabilities. Integration into your app or team collaboration. You can work locally and sync your changes when you push them. Easy querying, cleaning, visualization. You can integrate powerful version control and collaboration to your enterprise and individual customers. Remote data teams can collaborate on data projects easily.

Understanding the relationships between data is key to unlocking the true value of continuously changing business data. A graph database, unlike other databases, puts relationships first. It uses Linear Algebra and graph theory to explore and show how complex data webs, sources, and points relate. TIBCO®, Graph Database allows users to store, transform, and interpret complex dynamic data into meaningful insights. Users can quickly build data and computational models that create dynamic relationships between organizational silos. These knowledge graphs provide value by connecting the vast array of data in your organization and revealing relationships that allow you to optimize assets and processes. OLTP and OLAP features combined in a single enterprise-grade data base. Optimistic ACID-level transaction properties with native storage access.

RelationalAI is a next generation database system that allows intelligent data applications to be built on relational knowledge graphs. Data-centric application design combines logic and data into reusable models. Intelligent data applications can understand and make use each relation in a model. Relational provides a knowledge graph system that allows knowledge to be expressed as executable models. These models can easily be extended using declarative, human-readable software. RelationalAI's expressive and declarative language results in a 10-100x decrease in code. By involving non-technical domain specialists in the creation process, and automating complex programming tasks, applications are created faster and with better quality. The extensible graph data model is a foundation for data-centric architecture. Integrate models to uncover new relationships and reduce barriers between applications.

Luna is a subscription for Apache Cassandra support at DataStax. You can enjoy all the benefits offered by open-source Cassandra with the assurance that you have direct access the team that wrote the majority of the code. They also support some of the most important deployments around the globe. You will receive best practices, advice, as well as SLA-based support to maintain your Cassandra deployment. Scale without compromising performance or latency to manage the most complex real-time workloads. You can create highly interactive customer experiences that are real-time and highly interactive. Luna can help you resolve issues and follow best practices for Cassandra clusters. Services can be used to assist with the entire application life cycle. They also allow for deeper integration of your team as they work together on implementation.

Locstat, a graph intelligence platform, is a graph-based AI platform that integrates analytics, event processing and graph-based AI. It allows organizations to scale up next-generation data-driven solutions quickly. Research shows that adopting innovative AI-supported digitalization strategy can result in significant benefits and gains. We have had great success in improving customer efficiency and delivering significant ROI, as measured by ourselves and confirmed by research firms. This shows the value of advanced analytics technologies to solve today's complex issues more cost-effectively compared to solutions based on relational databases.