Best Graph Database Solutions - Page 5

mapgraph

mapgraph has a basic in-memory database for storing linked maps in Clojure and ClojureScript

nodegoat

OrigoDB

OrigoDB enables you to build high quality, mission critical systems with real-time performance at a fraction of the time and cost.

Average Rating: 3.5/5.0

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Puppy Graph

PuppyGraph is the first and only real time, zero-ETL graph query engine in the market, empowering data teams to query existing relational data stores as a unified graph model that deployed in under 10 minutes, bypassing traditional graph databases' cost, latency, and maintenance hurdles. Capable of scaling with petabytes of data and executing complex 10-hop queries in seconds, PuppyGraph supports use cases from enhancing LLMs with knowledge graphs to fraud detection, cybersecurity and more. Trusted by industry leaders, including Coinbase, Datadog, AMD, Netskope, Palo Alto Network, Baker Hughes, eBay, and more. Learn more at www.puppygraph.com.

Relay Framework

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 compiler, it keeps your iteration speed fast even as your app grows.

SAP Knowledge Graph

Drive high-performance business processes with AI that understands the full context of your data.

Sparksee

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.

ThingSpan enterprise graph and data fusion platform

ThingSpan is an enterprise-grade, massively scalable distributed platform designed for graph analytics and real-time relationship discovery. It enables organizations to analyze connections across multiple data sources in real-time, facilitating rapid navigation and pattern-finding within complex datasets. By integrating with industry-standard open-source technologies such as Apache Spark, Kafka, YARN, and Hadoop, ThingSpan ensures cost-effective scalability on commodity clusters. Its architecture supports both structured and unstructured data, allowing for immediate analysis of streaming data from IoT sensors and other sources, thereby providing timely insights and actionable intelligence. Key Features and Functionality: - Real-Time Graph Analytics: Facilitates immediate discovery of relationships and patterns within data, enabling swift decision-making. - Integration with Open-Source Technologies: Seamlessly works with Apache Spark, Kafka, YARN, and Hadoop, ensuring compatibility and scalability. - Support for Structured and Unstructured Data: Capable of ingesting and analyzing diverse data types from various sources, including IoT devices. - High-Speed Performance: Organizes data into real-world objects, eliminating inefficiencies associated with traditional relational databases and supporting data volumes beyond the petabyte level. - Parallel Ingestion and Analytics: Allows simultaneous data collection and analysis, reducing latency and enhancing responsiveness. Primary Value and Problem Solved: ThingSpan addresses the challenge of uncovering hidden relationships and patterns within vast and complex datasets in real-time. By providing a scalable and efficient platform for graph analytics, it empowers organizations to transform raw data into meaningful insights swiftly. This capability is crucial for applications requiring immediate interpretation of streaming data, such as those involving IoT sensors, where timely analysis can lead to proactive decision-making and competitive advantages.

Vaticle

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 language. Our technology helps organisations in various industries, including Life Sciences, Defence & Security, Financial Services and Robotics, to build intelligent systems that we believe will change the world.

Vertexdb

Vertex is a high performance graph database that supports automatic garbage collection, built on libevent and tokyocabinet.

xtendr

xtendr facilitates secure data sharing and collaboration between teams, departments, and organisations - generating powerful insights without ever compromising on privacy.
 Using a combination of best-in-market Privacy Enhancing Technologies (PETs), xtendr allows participants to securely share data with external analytics providers, as well as to pool their datasets in order to learn from a broader range of information - all without ever exposing personal or sensitive information. With applications in industries including healthcare, finance and manufacturing, xtendr allows collaborators to combine datasets for enhanced research, audience analysis, detection of patterns and trends, and more.