Graph databases use topographical data models to store data. These databases connect specific data points (nodes) and create relationships (edges) in the form of graphs that can then be pulled by the user with queries. Nodes can represent customers, companies, or any data a company chooses to record. Edges are formed by the database so that relationships between nodes are easily understood by the user. Businesses can utilize graph databases when they are pulling data and do not want to spend time organizing it into distinct relationships. Large enterprises may use complex queries to pull precise and in-depth information regarding their customer and user information or product tracking data, among other uses. Database administrators can scale high data values and still create usable models. Some businesses may choose to run an RDF database, a type of graph database that focuses on retrieving triples, or information organized in a subject-predicate-object relationship. Similar types of databases include document database tools, key-value store tools, object-orientated database tools and more. Developers who are looking for an affordable solution can look to free database software.
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Neo4j is the leading native graph database and graph platform. It is available a both open source and through a commercial license for enterprise levels of security and high performance and reliability through clustering. Neo4j's graph query language, Cypher is very easy to learn and can operate across Neo4j, Apache Spark and Gremlin-based products using newly released open source toolkits, "Cypher on Apache Spark (CApS) and Cypher for Gremlin. Neo4j also offers a complete graph platform that includes Neo4j Bloom for visual exploration, Neo4j ETL and Kettle integration for data integration, APOC procedures and graph analytic algorithm libraries and developer-friendly Neo4j Desktop development tools including Neo4j Browser.
One database. One Query Language. Three data models. Endless Possibilities. With more than one million downloads, ArangoDB is a fast growing native multi-model NoSQL database. It combines the power of graphs, with JSON documents and a key-value store. ArangoDB makes all of your data-models accessible with a single elegant declarative query language. ArangoDB is the simple, versatile and performant answer to many challenges facing developers, startups and enterprises in the near and far future. Simplifying complexity and increasing productivity is the mission of ArangoDB GmbH, the company behind the project. For more information, visit www.arangodb.com or follow us on Twitter @ArangoDB
OrientDB is the first Multi-Model Distributed DBMS with a True Graph Engine. Multi-Model means 2nd generation NoSQL able to manage complex domain with incredible performance. OrientDB manages relationships without using JOINs, but rather direct pointers. This allows to have constant performance on traversing relationships, no matter the database size.
Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.
Azure Cosmos DB provides native support for NoSQL choices, offers multiple well-defined consistency models, guarantees single-digit-millisecond latencies at the 99th percentile, and guarantees high availability with multi-homing capabilities and low latencies anywhere in the world.
HyperGraphDB is a general purpose, open-source data storage mechanism based on a powerful knowledge management formalism known as directed hypergraphs. While a persistent memory model designed mostly for knowledge management, AI and semantic web projects, it can also be used as an embedded object-oriented database for Java projects of all sizes. Or a graph database. Or a (non-SQL) relational database.
IBM Graph is a fully managed property graph-as-a-service that enables you to store, query and visualize data points, connections and properties. Highly available Provides service that is always up and ensures your data is always accessible, so your web and mobile apps are constantly working for your business. Managed 24/7 Our experts monitor, manage and optimize everything in your stack, every day, all day. Enables your development team to focus on building apps, instead of worrying about the graph. Scales seamlessly Lets you start small and scale on demand as your data size and complexity increases, enabling your application to grow with your business.
Oracle Spatial and Graph supports a full range of geospatial data and analytics for land management and GIS, mobile location services, sales territory management, transportation, LiDAR analysis and location-enabled Business Intelligence. The graph features include RDF graphs for applications ranging from semantic data integration to social network analysis to linked open data and network graphs used in transportation, utilities, energy and telcos and drive-time analysis for sales and marketing applications.
Combine the advantages of world-class database technology and the innovation of a vibrant open source community with Redis Enterprise. Gain pioneering high availability in the form of Active-Active and Active-Passive geographically distributed architectures, superb linearly scaling high performance and top-notch built-in search capabilities. Extend Redis databases to Flash SSDs for infrastructure cost-savings, and utilize your hardware to the maximum extent with Redis Enterprise. Grow your Redis databases efficiently with seamless scaling, automatic sharding and instant automatic failover. Redis Enterprise not only encompasses tunable levels of persistence and durability but it is also with reinforced with security controls, backups and auto-recovery. Extend the already versatile Redis databases to an infinite range of scenarios with integrated and custom Redis modules, which inherit all the platform advantages of Redis Enterprise.
AgensGraph is a new generation multi-model graph database for the modern complex data environment, which supports the relational and graph data models at the same time. AgensGraph supports ANSI-SQL and openCypher. SQL and Cypher can be integrated into single queries in AgensGraph.
Hundreds of billions of facts building the World Knowledge Graph are available in the Linked Open Data cloud. Even more are gathered as Proprietary Data Sets. Graph DB ™ is a semantic graph database that serves organizations to store, organize and manage content in the form of semantically enriched smart data. A non-relational (NoSQL) database, Graph DB handles massive loads, queries, and inferencing in real time. This allows for seamless integration of disparate data silos and a holistic 360-degree view of information. In essence, Graph DB is a “semantic repository”, a database system used for storage, querying, and management of structured data. It uses ontologies to automatically reason about data. Smart data management means better information products and services at a faster speed. This is achieved by handling information in a way that allows data items to be seamlessly mixed, exposed and shared across different platforms. As a result, their relationships mapped with a high level of detail. With Graph DB your data is stored in atomic facts that are easy to classify, reuse, combine share and integrate. Something more, such a mechanism for storing and managing data allows the creation of new facts that are implied in data. How Graph DB Enables Smart Data Management? Data integration and interlinking - The ability to recognize entities across multiple sources hold great promise helping to manage your data more effectively and pinpointing connections in your data that may be masked by slightly different entity references. Single interconnected information space formed by structured data and text documents - Materialised connections between free-flowing, unstructured text, and data facts stored as database entities are extremely valuable. Such connections link entities from the database to the documents that mention them denoting relationships from which they were extracted. Linked Open Data Compatibility - Linked Data is an approach for publishing and interlinking data on the web to improve data interchange. The amount of openly released dataset grows exponentially in the recent years, covering everything from music, places, subjects of interest and products, to highly specific domains as drugs and bibliography. When applied correctly, semantic facts can enhance your knowledge management and data discovery applications. You can answer more queries much faster and the results of the queries are highly relevant to your search. W3C standards compliance - Graph DB stores semantic facts in the form of subject - predicate - object using the Resource Description Framework. RDF is a standard model for data publishing and interchange on the Web. RDF has features that facilitate data merging even if the underlying schemas differ. Expressive, rich and flexible data model - Graph DB supports all forms of metadata classification of data, express as ontologies, where ontologies are equated but limited to thesauri, taxonomic hierarchies of classes, class definitions and relations. Reasoning - Graph DB can infer new knowledge from existing facts. This is called inferencing. Why is this important? You can create new facts from existing facts. Your queries run faster. Your results are more accurate. Not all graph databases support this capability and some apply different techniques to infer new semantic facts. The applications of inferencing span industries and use cases.
Blazegraph is a scalable, high-performance graph database with support for the Blueprints and RDF/SPARQL APIs. Blazegraph is available in a range of versions that provide solutions to the challenge of scaling graphs. Blazegraph solutions range from millions to trillions of edges in the graph.
BrightstarDB is an RDF triple store. It does not require the definition of a database schema, and with the RDF data model model you can easily add and integrate data of all shapes. We also implement the standard SPARQL query language, update language and protocol so you can use off-the-shelf client tools to connect to your data stores.
Graph Engine (GE) is a distributed, in-memory, large graph processing engine, underpinned by a strongly-typed RAM store and a general computation engine. The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set.
Stardog is a reusable, scalable knowledge graph platform that enables enterprises to unify all their data, including data sources and databases of every type, to get the answers needed to drive business decisions. Stardog is an enterprise knowledge graph platform that allows customers to query massive, disparate, heterogeneous data regardless of structure with simplicity of implementation. Stardog’s enterprise customers include Fortune 500 companies in finance, healthcare, life sciences, energy, media, and government.
TripleBit is a fast and compact system for large scale RDF graph. TripeBit is designed with a compact storage and index structure, and it does not only reduces the size of indexes (e.g., through compression techniques) but also minimizes the number of indexes used in query evaluation.