Open Source Graph Databases - Page 2
Sort By:
Graph Databases
View 53 business solutions-
Simple, Secure Domain RegistrationRegister or renew your domain and pay only what we pay. No markups, hidden fees, or surprise add-ons. Choose from over 400 TLDs (.com, .ai, .dev). Every domain is integrated with Cloudflare's industry-leading DNS, CDN, and free SSL to make your site faster and more secure. Simple, secure, at-cost domain registration.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
1
PyMongo
PyMongo - the Official MongoDB Python driver
The PyMongo distribution contains tools for interacting with MongoDB database from Python. The bson package is an implementation of the BSON format for Python. The pymongo package is a native Python driver for MongoDB. The gridfs package is a gridfs implementation on top of pymongo. -
2
SurrealDB
A scalable, distributed, collaborative, document-graph database
With an SQL-style query language, real-time queries with highly-efficient related data retrieval, advanced security permissions for multi-tenant access, and support for performant analytical workloads, SurrealDB is the next generation serverless database. SurrealDB is the ultimate cloud database for tomorrow's applications. SurrealDB is an innovative NewSQL cloud database, suitable for serverless applications, jamstack applications, single-page applications, and traditional applications. It is unmatched in its versatility and financial value, with the ability for deployment on cloud, on-premise, embedded, and edge computing environments. For a hassle-free setup, get started with SurrealDB Cloud in one-click. There is no need for your team to learn new complicated database languages. Getting started with SurrealDB is as simple as one command - and advanced functionality is simple to understand, whilst still being fast and performant. -
3
Testcontainers node
Supports tests, providing lightweight, throwaway instances of database
Testcontainers is an open source library for providing throwaway, lightweight instances of databases, message brokers, web browsers, or just about anything that can run in a Docker container. No more need for mocks or complicated environment configurations. Define your test dependencies as code, then simply run your tests and containers will be created and then deleted. With support for many languages and testing frameworks, all you need is Docker. Use a containerized instance of your database to test your data access layer code for complete compatibility, without requiring a complex setup on developer machines. Trust that your tests will always start with a known state. Use containerized web browsers, compatible with Selenium, to run automated UI tests. Each test gets a fresh, clean instance of the browser, without having to worry about variations in plugins or required updates. -
4
Resources
Relative Examples and Resources, etc...
IOs, Graph Database Wrapper and more examples for iBoxDB Download Mirrors https://sourceforge.net/settings/mirror_choices?projectname=application-database&filename=iboxdb392.zip&selected=pilotfiber -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
5
ChartDB-Desktop
ChartDB Desktop Version
Download ChartDB for desktop | Descarga ChartDB para escritorio This project is an opensource fork of ChartDB, adapted to desktop. It contains ChartDB functionalities but no AI integration. For this, the Electron library was used. Este proyecto es un fork opensource de ChartDB, adaptado al escritorio. Contiene las funcionalidades de ChartDB pero sin integración AI. Para ello, se utilizó la biblioteca Electron. -
6
Graph Portal
Software to build web portals using concepts of Graph Theory
Graph Portal allows to organize many types of data so that you can get the information you want in the easiest way possible. There are many use cases for this software, including: building a web portal, creating mind map, wiki, studying graph theory concepts and algorithms,content management etc. The application has built-in Lucene search, custom query, file attachment to nodes etc. Graph Portal comes with an optional built-in security for authentication and role-based authorization. There are multiple levels of customization (depending on the technical skills of the customer) for branding the implementation to customer needs. The software exposes RESTFul services for integration with third-party user interfaces. This is a web based software and can be accessed from computers, tablets and phones from anywhere. -
7
Cosmos DB Spark
Apache Spark Connector for Azure Cosmos DB
Azure Cosmos DB Spark is the official connector for Azure CosmosDB and Apache Spark. The connector allows you to easily read to and write from Azure Cosmos DB via Apache Spark DataFrames in Python and Scala. It also allows you to easily create a lambda architecture for batch-processing, stream-processing, and a serving layer while being globally replicated and minimizing the latency involved in working with big data. -
8
Crase enables instant access to structured information (personal contacts, software modelling, ..., whatever). The functionality is based on the mix of techniques, such as the graph database, metadata and functionality from composition and others.
-
9
CrispI
A hybrid graph database with analysis
A graph database layer in Java which allows mixed-mode database handling. For example, using an underlying oodb, a OneToMany and ManyToOne relationships can be implemented allowing quick and robust hierarchies to be built (a subset of a graph). The system also includes a Big Data implementation, other analytics and visualization. The current invokation is based on a Versant Db. Examples for use can be found in CrispI-Examples. -
Connect every part of your business to one bank accountNorth One is proudly built for small businesses, startups and freelancers across America. Make payments easily, keep tabs on your money and put your finances on autopilot through smart integrations with the tools you’re already using. North One was built to make managing money easy so you can focus on running your business. No more branches. No more lines. No more paperwork. Get complete access to your North One Account from your phone or computer, wherever your business takes you. Create Envelopes for taxes, payroll, rent, and anything else automatically.
-
10
**Zastosowanie**: Głównym celem projektu jest optymalizacja wykorzystania pamięci w graficznych bazach danych poprzez analizę podobieństwa cosinusowego wektorów cech obrazów. Umożliwia identyfikację duplikatów (obrazów bardzo do siebie podobnych), co prowadzi do efektywniejszego zarządzania zasobami. **Program** W chwili obecnej program przeszukuje obrazy w katalogu, do którego ścieżka została zapisana w pliku 'Data\settings.txt'. **Użycie**: Program obsługuje się w terminalu. Po piewszym wykonaniu programu, zostaje utworzony katalog Data, w miejscu gdzie zapisany jest program. Aby wybrać ścieżkę do katalogu z obrazami należy w pliku 'settings.txt' zapisać ścieżkę. Następnie można wykonywać program z: -an, -mnb, -c, -i Link do GitHub: https://github.com/Duke-Axer/Duplicate-Finder Wszystkie pytania proszę pisać na b.gabka.nkn@gmail.com
-
11
EdgeDB
A next-generation graph-relational database
The world's favorite graph-relational database is back for an action-packed sequel. Tune in for a series of lightning talks breaking down what's new and what's next for EdgeDB. EdgeDB is an open-source database designed as a spiritual successor to SQL and the relational paradigm. It aims to solve some hard design problems that make existing databases unnecessarily onerous to use. 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. It's like a relational database with an object-oriented data model, or a graph database with strict schema. We call it a graph-relational database. The core unit of schema in the graph-relational model is the object type, analogous to a table in SQL. Object types contain properties and can be linked to other object types to form a schema graph. -
12
GUN
Cybersecurity protocol for syncing decentralized graph data
GUN is a small, easy, and fast data sync and storage system that runs everywhere JavaScript does. The aim of GUN is to let you focus on the data that needs to be stored, loaded, and shared in your app without worrying about servers, network calls, databases, or tracking offline changes or concurrency conflicts. This lets you build cool apps fast. When a browser peer asks for data, it'll merge the reply with its own data using a CRDT, then cache the result. GUN is fully decentralized (peer-to-peer or multi-master), meaning that changes are not controlled by a centralized server. A server can be just another peer in the network, one that may have more reliable resources than a browser. You save data on one machine, and it will sync it to other peers without needing a complex consensus protocol. It just works. GUN can be used in both browsers and servers. We have made it easy to install in many different environments. -
13
Graph Database
A robust, reliable, user-friendly, and high-performance Graph Database
This is an academic project to build a graph database, supporting multiple users, with fully functioned data query, data manipulation and indexing mechanism. It is running in a distributed client-server mode. This project is assigned to final-year project from group IT121B, Bachelor of Computing, University of Wollongong. -
14
Graph Notebook
Library extending Jupyter notebooks to integrate with Apache TinkerPop
The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop, openCypher or the RDF SPARQL graph models. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs. This project includes many examples of Jupyter notebooks. It is recommended to explore them. All of the commands and features supported by graph notebook are explained in detail with examples within the sample notebooks. You can find them here. As this project has evolved, many new features have been added. If you are already familiar with graph-notebook but want a quick summary of new features added, a good place to start is the Air-Routes notebooks in the 02-Visualization folder. -
15
Greenmask
PostgreSQL database anonymization and synthetic data generation tool
Greenmask is a powerful open-source utility that is designed for logical database backup dumping, obfuscation, and restoration. It offers extensive functionality for backup, anonymization, and data masking. Greenmask is written in pure Go and includes ported PostgreSQL libraries that allows for platform independence. This tool is stateless and does not require any changes to your database schema. It is designed to be highly customizable and backward-compatible with existing PostgreSQL utilities. The Greenmask utility plays a central role in the Greenmask ecosystem. Our goal is to develop a comprehensive, UI-based solution for managing obfuscation procedures. We recognize the challenges of maintaining obfuscation consistency throughout the software lifecycle. Greenmask is dedicated to providing valuable tools and features that ensure the obfuscation process remains fresh, predictable, and transparent. -
16
Grinn
graph database and R package for omic data integration
http://kwanjeeraw.github.io/grinn/ -
17
HugeGraph
A graph database that supports more than 100+ billion data
HugeGraph is a convenient, efficient, and adaptable graph database compatible with the Apache TinkerPop3 framework and the Gremlin query language. HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges Graph, millisecond-level OLTP query capability, and can be integrated into big data platforms like Hadoop or Spark for OLAP analysis. The main scenarios of HugeGraph include correlation search, fraud detection, and knowledge graph. Not only supports Gremlin graph query language and RESTful API but also provides commonly used graph algorithm APIs. To help users easily implement various queries and analyses, HugeGraph has a full range of accessory tools, such as supporting distributed storage, data replication, scaling horizontally, and supports many built-in backends of storage engines. -
18
IP database REST API
Open Source REST API for IP database, includes qqwry/ipipdotnet
Open Source REST API for IP database, includes qqwry, ipipdotnet. -
19
Nebula Graph
A distributed, fast open-source graph database
The graph database built for super large-scale graphs with milliseconds of latency. Optimized SUBGRAPH and FIND PATH for better performance. Optimized query paths to reduce redundant paths and time complexity. Optimized the method to get properties for better performance of MATCH statements. Nebula Graph adopts the Apache 2.0 license, one of the most permissive free software licenses in the world. Free as in freedom, because, under the Apache 2.0 license, you can use, copy, modify and redistribute Nebula Graph, even for commercial purposes, all without asking for permission. We believe that great open source projects are not built in isolation, but rather by a community of contributors. We welcome contributions to Nebula Graph from anyone regardless of skill level or background in software development. If you have an idea for a feature you would like to see added, or you have identified a bug that needs fixing, please don't hesitate to submit an issue to our Github repository. -
20
-
21
STON
Translator of SBGN PD and AF maps into Neo4j graph database.
STON, Sbgn TO Neo4j, is a Java-based framework that imports and translates metabolic, signalling and gene regulatory pathways presented in SBGN (Systems Biology Graphical Notation, http://www.sbgn.org) Process Description (PD) and Activity Flow (AF) languages to a graph-oriented format compatible with Neo4j (http://neo4j.com/). STON1.2 allows also to link common processes between two different PD maps. This framework uses two sets of libraries: LibSBGN and Neo4j libraries. Requirements: Java 1.8 is necessary. Works with Neo4j Community Edition 2.3.1 -
22
SGDB is an embedded graph database written in JAVA. Goal of SGDB is to provide decent performance for breadth traversal processing even for larger graphs.
-
23
TRAK Metamodel
Tuples (triples) for TRAK architecture viewpoints and views
The definition of the metamodel for TRAK (defines allowed AD elements and relationships i.e. tuples/ triples for the TRAK viewpoints and views). TRAK is a general systems-thinkers'/system engineering enterprise architecture framework. It is simple, user-friendly, pragmatic and not limited to IT. 100% triple-centric and semantically-sound. Forms basis for RDF + OWL ontology description - see https://trakmetamodel.sourceforge.io/vocab/TRAK_metamodel.html. Each TRAK metamodel element now has its own web page - see https://trakmetamodel.sourceforge.io/metamodel/index.html -
24
TRAK Viewpoints
Specifications for TRAK architecture views
The architecture viewpoints (specifications for architecture views iaw ISO 42010) for TRAK. TRAK is a general systems-thinkers'/system engineering enterprise architecture framework. It is simple, user-friendly, pragmatic and not limited to IT. 100% triple-centric and semantically-sound. Defines a total of 24 viewpoints. The ones needed are selected by taking the task sponsor's concerns and matching them to the typical concerns that each TRAK viewpoint addresses. The triples that address each concern are defined in the TRAK metamodel (https://sf.net/p/trakmetamodel). The mapping between metamodel triple and architecture viewpoint is held with a Neo4J graph model (https://doi.org/10.1002/eng2.12168) and defined using (more) architecture viewpoints (https://www.researchgate.net/publication/335176248_Architecture_Description_Viewpoints_Metamodel_Description_Implementation_and_Model_Changes). The minimal process is defined in the overall TRAK specification (https://sf.net/p/trak) -
25
Wikipedia Concept Association Map (WCAM) is new approach for textual knowledge representation and understanding. All concepts and associations are stored in a graph database for better performance and easy distribution.