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Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purp
ArangoDB is the company behind the leading, multi-model graph data and analytics platform that uncovers insights that are difficult or impossible with traditional SQL, Document, or even legacy graph d
The Apollo Graph Platform unifies GraphQL across apps and services, unlocking faster delivery for engineering teams
The fastest path to graph. Centered around the leading native graph database, today's Neo4j Graph Data Platform is a suite of applications and tools helping the world make sense of data. The Platfor
Serverless, self-serve and affordable analytics designed to help you get the most out of your data.
See the Value in Your Data. Flexible analytics and visualization platform. Real-time summary and charting of streaming data. Intuitive interface for a variety of users. Instant sharing and embedding o
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 relation
DataStax is the company that powers generative AI applications with real-time, scalable data and production-ready vector data tools that generative AI applications need, and seamless integration with
TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering o
FlockDB is simpler than other graph databases because it tries to solve fewer problems. It scales horizontally and is designed for on-line, low-latency, high throughput environments such as web-sites.
GraphBase is a second generation Graph Database Management System (DBMS). Built for 21st Century data problems, GraphBase is a game-changer when it comes to handling large, complex data structures.
Discover powerful SharePoint Solutions for your business needs. From document management to team collaboration, our expert team can customize SharePoint to fit your unique requirements. Contact us t
Dgraph is the world's most advanced GraphQL database with a graph backend. The number one graph database on GitHub and over 500,000 downloads every month, Dgraph is built for performance and scalabili
GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clien
Redis Cloud is our fully-managed Redis Enterprise service, delivering unmatched speed, simplicity, and scalability. It's perfect for cloud-native applications requiring real-time data processing, with
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 busine
Cayley is an open-source graph written in Go inspired by the graph database behind Freebase and Google's Knowledge Graph.
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 an
Powered by the Postgres query engine under the hood, EdgeDB thinks about schema the same way you do: as objects with properties connected by links.
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 lo
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 av
Fauna is a truly serverless operational database that empowers teams to ship applications faster. It combines the flexibility of a document model with the strong consistency and rich querying power of
HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to Ap
RDFox is a high-performance in-memory knowledge graph and semantic reasoner. Optimised for speed and advanced reasoning, it affords query and loading times that are orders of magnitudes faster than al
Ultipa builds category-defining real-time graph XAI & database products, and empowers smart enterprises with graph augmented intelligence. Enterprises around the world are going through a major d
data.world is the most-adopted data catalog and governance platform on the market. Built on a unique knowledge graph foundation, data.world seamlessly integrates with your existing systems. We set
An ultra-low latency Graph Database that perfects the Knowledge Graph for GraphRAG. Effectively overcoming the existing limitations of RAG for GenAI & Large Language Models (LLM).
Redis Software is our advanced solution delivering unmatched speed and reliability for on-prem and private cloud environments. It gives you full control over your deployment, ensuring high performance
AllegroGraph® is a modern, high-performance, persistent graph database. AllegroGraph uses efficient memory utilization in combination with disk-based storage. AllegroGraph supports SPARQL, RDFS++, and
Bitsy is a small, fast, embeddable, durable in-memory graph database that implements the Blueprints API.
https://www.connectedpapers.com is a unique, visual tool to help researchers and applied scientists find and explore papers relevant to their field of work. It started as a weekend side project be
HGraphDB is a client layer for using HBase as a graph database. It is an implementation of the Apache TinkerPop 3 interfaces.
ArchiGraph is an ontology-based data management and data virtualization platform. It includes a collaborative ontology editor, a SHACL constraints and rules construction tool, and a middleware layer p
BaseQL provides a dynamic GraphQL API for Airtable bases and Google Sheets. BaseQL is built for speed of development without the hassle of a managed database or complicated REST endpoints. It enable
Ontotext GraphDB ™ allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs. The database is the preferred choice of both small ind
GUN is a realtime, distributed, offline-first, graph database engine. Lightweight and powerful, at just ~9KB gzipped.
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
Memgraph is an open source graph database built for real-time streaming and compatible with Neo4j. Whether you're a developer or a data scientist with interconnected data, Memgraph will get you the im
A truly distributed, linear scalable, lightning-fast graph database.
RecallGraph is a versioned-graph data store - it retains all changes that its data (vertices and edges) have gone through to reach their current state. It supports point-in-time graph traversals, lett
Timbr.ai merges the most advanced knowledge graph technology with the SQL ecosystem. Timbr helps some of the biggest companies in the world make faster, better decisions with their data modeled as a
VelocityGraph extends the object-oriented database VelocityDB into a graph database.
Discover one of the fastest, most scalable, distributed graph databases purpose-built for high-performance analytical insights across the enterprise. Solving your complex connected data problems at re
Apache AGE® is a PostgreSQL extension that provides graph database functionality. The goal of Apache AGE® is to provide graph data processing and analytics capability to all relational databases. Th
ApertureDB is a database purpose-built for multimodal AI. It combines the functionalities of a vector database, graph database, and multimodal data management to allow users to manage all their data w
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
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
TerminusDB is an open source in-memory graph database designed for the web age. TerminusDB makes a radical departure from historical architectures. First, we implement a graph database with a strong
TIBCO Graph Database allows you to discover, store, and convert complex dynamic data into meaningful insights.
The Aerospike Real-time Data Platform enables organizations to act instantly across billions of transactions while reducing server footprint by up to 80 percent. The Aerospike multi-cloud platform pow
Aerospike Graph is a high-performance, distributed graph database designed to manage and query extensive graph datasets with exceptional speed and scalability. Built upon the robust Aerospike Database
BangDB is a platform that provides an end-to-end solution for real-time big data analytics process.
The fastest and most usable knowledge graph database. Launch intelligent products in record time. Data Graphs is the fastest enterprise Knowledge Graph Database platform, both in performance and in
Grafbase is a GraphQL platform designed for managing federated graphs in enterprise environments, across distributed systems. It provides a unified gateway that allows teams to compose APIs from multi
Graph Story provides graph databases, applications and solutions as a service.
JanusGraph is a scalable graph database optimized for storing and querying highly-interconnected data and provides you with simple and efficient data retrieval from complex structures
Macrometa is a hyper-distributed cloud platform featuring a Global Data Network (GDN) and PhotonIQ, an AI-powered Edge Delivery Network. With over 175 points of presence (PoPs) worldwide, Macrometa em
mapgraph has a basic in-memory database for storing linked maps in Clojure and ClojureScript
OrigoDB enables you to build high quality, mission critical systems with real-time performance at a fraction of the time and cost.
Relay is designed for high performance at any scale. Relay keeps management of data-fetching easy, whether your app has tens, hundreds, or thousands of components. And thanks to Relay’s incremental co
Sparksee (formerly known as DEX) is a graph database that makes space and performance compatible with a small footprint and a fast analysis of large networks.
Vaticle is a team of people driven by a purpose: to solve the world's most complex problems, through knowledge engineering. We are the inventors of the Grakn knowledge-base and the Graql query languag
Vertex is a high performance graph database that supports automatic garbage collection, built on libevent and tokyocabinet.
xtendr facilitates secure data sharing and collaboration between teams, departments, and organisations - generating powerful insights without ever compromising on privacy. Using a combination of bes
Graph databases are designed for depicting relationships (edges) between data points (nodes). Less structurally rigid than relational databases, graph databases allow nodes to have a multitude of edges; that is, there’s no limit on the number of relationships a node can have. (An example of this is in the following section.) Additionally, each edge can have multiple characteristics which define it. There is no formal limit—nor standardization—on how many edges each node can have, nor how many characteristics an edge can have. Graph databases can also contain many different pieces of information that would not necessarily be normally related.
Each node is defined by pieces of information called properties. Properties could be names, dates, identification numbers, basic descriptors, or other information—anything that would describe the node itself. Nodes are connected by edges, which can be directed or undirected. Like in mathematical graph theory, an undirected edge is bidirectional; that is, a relationship can be carried from node A to node B, and from node B to node A. A directed edge, however, only carries meaning in one direction, say from node B to node A.
Key Benefits of Graph Databases
Graph databases are ideal for storing and retrieving information that is independent but related in multiple ways. For example, say a user wanted to map a group of friends. Each friend would be a node, with edges between each friend with a characteristic “friends." But, say two of those friends are coworkers; then, their edge would also have a characteristic “coworkers." Edges can get further definition by adding common interests, personal experiences, and so on.
Because graph databases are, by design, most conducive to organizing broad sets of data through which there are not uniform relationships or kinds of data, they can be invaluable tools for social mapping, master data management, knowledge graphing/ontology, infrastructure mapping, recommendation engines, and more. A business could set each node to be one of their products, and let edges draw recommendation relationships based on what product a consumer might buy. It could also map relationships between contacts, departments, and more.
Graph databases are flexible and scalable by design, so a business user would not need to know an exact or complete use case for a graph database before creating it. Expanding a graph database is a matter of adding new nodes and any potential edges which might be associated with them.
Like other databases, graph databases are primarily maintained by a database administrator or team. That said, because of their wide range of coverage, graph databases are often accessed by several organizations within a company. Development, IT, billing, and more would all have valid reasons for needing access to graph databases, pending their assigned uses within the company.
Graph database solutions will typically have the following features.
Database creation and maintenance — Graph databases allow users to easily build and maintain a database(s).
CRUD operations — An acronym for create, read, update, and delete, CRUD operations delineate basic operations of many databases. Graph databases should be able to perform these operations and usually can with similar capability to the most notable CRUD-oriented database type, relational.
Scalability and flexibility — Graph databases can grow and expand with business requirements. Unlike some other database solutions, they can scale more quickly with less worry about strict data organization, relying instead on developing relationships between new and existing nodes.
Simplified querying — Graph databases can skip some larger query complexities, bypassing things like foreign keys, nested queries, and join statements in favor of direct or transitive relationships.
OS compatibility — Graph databases do not require any one specific operating system to run, making them a flexible choice for any operating system.
Security and privacy — As alluded to above, graph databases can struggle with security and privacy situations. They require more strict implementations of security and access measures. Since graph databases are more oriented toward mapping relationships, that structure can also be utilized in ways that could raise privacy concerns, such as revealing a more laid-bare view of a client or customer—and every other potential client or customer to which they are related. Businesses implementing graph databases should take extra care to secure both how these databases are accessed, and the databases themselves.
Data integrity implications — Graph databases simplify the ways in which information relates to other information. In doing so, by shortening or condensing the relationship (as compared to, say, traversing numerous tables in a relational database), it’s particularly vital that all data in a graph database is accurate. One improperly aligned relationship can directly lead to incorrect data, unlike in a relational database where improper data might hit a snag during a nested query, throw an error, and out the issue. So, in using graph databases, data integrity is of particularly high importance.