Open Source Windows Graph Databases
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Graph Databases for Windows
View 20 business solutionsBrowse free open source Graph Databases and projects for Windows below. Use the toggles on the left to filter open source Graph Databases by OS, license, language, programming language, and project status.
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Blazegraph (powered by bigdata)
Fast, scalable, robust graph database platform
Blazegraph has moved to Github. Please see https://github.com/blazegraph/database/. -
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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. -
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OWASP Amass
In-depth attack surface mapping and asset discovery
The OWASP Amass Project has developed a tool to help information security professionals perform network mapping of attack surfaces and perform external asset discovery using open source information gathering and active reconnaissance techniques. The Open Web Application Security Project (OWASP) is a nonprofit foundation that works to improve the security of software. All of our projects ,tools, documents, forums, and chapters are free and open to anyone interested in improving application security. The volume argument allows the Amass graph database to persist between executions and output files to be accessed on the host system. The first field (left of the colon) of the volume option is the amass output directory that is external to Docker, while the second field is the path, internal to Docker, where amass will write the output files. -
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Spring Data Neo4j
Provide support to increase developer productivity in Java
Spring Data Neo4j, part of the larger Spring Data family, provides easy configuration and access to Neo4j Graph Databases from Spring applications. It offers three different levels of abstraction to access the store. The Neo4j client, the Neo4j Template, and the Neo4j Repositories. Spring Data Neo4j offers advanced features to map annotated entity classes to the Neo4j Graph Database. The template programming model is equivalent to other Spring templates and builds the basis for interaction with the graph and is also used for the Spring Data repository support. Spring Data Neo4j is a core part of the Spring Data project which aims to provide convenient data access for NoSQL databases. Spring Data builds on Spring Framework, check the spring.io web-site for a wealth of reference documentation. If you are just starting out with Spring, try one of the guides. -
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Neovim DBee
Interactive database client for Neovim
Interactive database client for Neovim. This project aims to be as cross-platform as possible, but there are some limitations (for example some of the go dependencies only work on certain platforms). To address this issue, the client implementations are detached from the main logic and they register themselves to DBee backend on plugin start. This allows the use of build constraints, which we use to exclude certain client implementations on certain platforms. You can pass an optional table parameter to the setup() function. -
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sqlite-vec
A vector search SQLite extension that runs anywhere
A vector search SQLite extension that runs anywhere. -
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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 -
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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. -
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ArcticDB
ArcticDB is a high performance, serverless DataFrame database
Built for the modern Python Data Science ecosystem, ArcticDB transforms your ability to handle complex real-world data with an Incredibly fast proven Petabyte scale. ArcticDB is designed with quant users in mind. It allows you to self-manage your data leveraging your preferred infrastructure. Giving you the keys to protect your most valuable asset. Supports large concurrent writes to many tables ensuring datasets can be onboarded fast and in the most convenient format. Scale-out architecture and built-in compression ensure that networks and storage are utilized efficiently. Support for DataFrames with thousands of columns enables large trading universes. -
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Bytebase
The GitHub/GitLab for database DevSecOps
The GitHub/GitLab for database DevSecOps. World's most advanced database DevSecOps solution for Developer, Security, DBA and Platform Engineering teams. -
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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. -
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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. -
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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. -
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asami
A flexible graph store, written in Clojure
Asami is now being developed in this repository, as it is no longer being supported at Cisco. The deployment to Clojars has not changed, as it was always to my personal account. -
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pg_analytics
DuckDB-powered analytics for Postgres
pg_analytics (formerly named pg_lakehouse) puts DuckDB inside Postgres. With pg_analytics installed, Postgres can query foreign object stores like AWS S3 and table formats like Iceberg or Delta Lake. Queries are pushed down to DuckDB, a high-performance analytical query engine. By transforming Postgres into a performant search and analytics engine, ParadeDB frees your team from the pain of scaling and syncing Elasticsearch. -
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tigerbeetle
The financial transactions database designed for mission critical safe
TigerBeetle is production-ready on Linux and seamlessly integrated with major programming languages. TigerBeetle is a financial transactions database designed for mission-critical safety and performance to power the next 30 years of OLTP. TigerBeetle redesigns the distributed database storage engine and consensus protocol for the OLTP workload. This solves the problem of OLTP write contention to unlock three orders of magnitude more performance than a general purpose (OLGP) database. The language of business transactions around the world is Debit/Credit. TigerBeetle ships with this schema out of the box. A single TigerBeetle cluster can process over 100 billion transactions, at a hundredth of the cost compared to legacy, proprietary, or cloud databases. -
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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) -
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iBoxDB Resources
Relative Examples and Resources of iBoxDB, etc...
IOs, Graph Database Wrapper and more examples for iBoxDB Download Mirrors https://sourceforge.net/settings/mirror_choices?projectname=application-database&filename=iboxdb412.zip&selected=pilotfiber -
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Common Resource Grep - crgrep
Common Resource Grep
CRGREP searches for matching text in databases, various document formats, archives and other difficult to access resources. A command line tool for name and content text matching in database tables, plain files, MS Office documents, PDF, archives, MP3 audio, image meta-data, scanned documents, maven dependencies and web resources. CRGREP will search resources within resources of any arbitrary combination or depth, so text within a document within a zip archive, and so on. Here you will find binary downloads and discussion (https://sourceforge.net/p/crgrep/discussion/) . The actual development and issue tracking can be found here: https://bitbucket.org/cryanfuse/crgrep -
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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. -
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AdminJS
AdminJS is an admin panel for apps written in node.js
Our system leverages AI to effortlessly generate text. Simplify your product database management with just one click. The out-of-the-box version of AdminJS is pretty powerful, but its in-depth customizability is where it really shines. With a basic knowledge of React and Node.js, you can change nearly every behavior of your admin panel. With AdminJS you can Create, Read, Update, and Delete all of your resources, no matter where they come from. Thanks to the tight integration with your ORM/ODM, AdminJS picks up all the validation rules, data types, and relationships. AdminJS has a simple interface for connecting multiple data sources like SQL/noSQL databases and multiple APIs. We already created adapters for the most popular services. With AdminJS, you can easily find any record inside your dataset. Everything thanks to the advanced filters panel which is able to narrow your search according to multiple criteria. -
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Backup Tables
Backup single or multiple database tables with ease
Backup single or multiple database tables with ease. Use the BackupTables::generateBackup($tableToBackup) Facade anywhere in your application and it will generate $tableToBackup_backup_2024_08_22_17_40_01 table in the database with all the data and structure. Note that the datetime 2024_08_22_17_40_01 will be varied based on your datetime. -
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BadgerDB
Fast key-value DB in Go
BadgerDB is an embeddable, persistent and fast key-value (KV) database written in pure Go. It is the underlying database for Dgraph, a fast, distributed graph database. It's meant to be a performant alternative to non-Go-based key-value stores like RocksDB. Badger is stable and is being used to serve data sets worth hundreds of terabytes. Badger supports concurrent ACID transactions with serializable snapshot isolation (SSI) guarantees. A Jepsen-style bank test runs nightly for 8h, with --race flag and ensures the maintenance of transactional guarantees. Badger has also been tested to work with filesystem-level anomalies, to ensure persistence and consistency. Badger is being used by a number of projects including Dgraph, Jaeger Tracing, UsenetExpress, and many more. BadgerDB is a pretty special package from the point of view that the most important change we can make to it is not on its API but rather on how data is stored on disk. -
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Cayley
An open-source graph database
Cayley is a free and open source database for Linked Data. Inspired by the graph database behind Google's Knowledge Graph (formerly Freebase), it offers a similar experience for Freebase fans and more. Cayley has a built-in query editor, visualizer and REPL, as well as support for multiple Query languages, including Gizmo (Gremlin dialect), MQL and GraphQL dialect. Cayley is also highly modular, so you can easily extend and connect it to your favorite programming languages and back-end stores. Cayley works on top of your existing database no matter the data model. It’s been well tested by numerous companies for their production workloads and is therefore very production-ready. -
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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.
