Open Source Linux Graph Databases
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Graph Databases for Linux
View 18 business solutionsBrowse free open source Graph Databases and projects for Linux 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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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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Dokploy
Open Source Alternative to Vercel, Netlify and Heroku
Streamline your operations with our all-in-one platform, perfect for managing projects, data, and system health with simplicity and efficiency. Simplify your project and data management, ensure robust monitoring, and secure your backups—all without the fuss over minute details. Elevate your infrastructure with tools that offer precise control, detailed monitoring, and enhanced security, ensuring seamless management and robust performance. Streamline your deployments with our PaaS. Effortlessly manage Docker containers and traffic with Traefik. Boost your infrastructure's efficiency and security today -
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Valkey
A flexible distributed key-value datastore
Valkey is an open source (BSD) high-performance key/value datastore that supports a variety of workloads such as caching, and message queues, and can act as a primary database. Valkey can run as either a standalone daemon or in a cluster, with options for replication and high availability. Valkey natively supports a rich collection of datatypes, including strings, numbers, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, and more. You can operate on data structures in-place with an expressive collection of commands. Valkey also supports native extensibility with built-in scripting support for Lua and supports module plugins to create new commands, data types, and more. -
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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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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. -
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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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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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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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Lada Cache
A Redis based, fully automated and scalable database cache layer
A Redis-based, fully automated, and scalable database cache layer for Laravel. -
11
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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Nestjs boilerplate Microservice API
Nestjs boilerplate microservice api
A boilerplate for building microservices with NestJS, this provides a template with essential components for microservice architecture. -
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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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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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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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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 -
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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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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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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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falkordb
A super fast Graph Database
A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG). -
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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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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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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. -
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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.