Browse free open source Graph Databases and projects below. Use the toggles on the left to filter open source Graph Databases by OS, license, language, programming language, and project status.
ArcticDB is a high performance, serverless DataFrame database
A vector search SQLite extension that runs anywhere
Interactive database client for Neovim
In-depth attack surface mapping and asset discovery
Nestjs boilerplate microservice api
The financial transactions database designed for mission critical safe
AdminJS is an admin panel for apps written in node.js
Stream, transform, and route PostgreSQL data in real-time
Generate your migrations from an existing database schema
Backup single or multiple database tables with ease
PostgreSQL database anonymization and synthetic data generation tool
Supports tests, providing lightweight, throwaway instances of database
A flexible distributed key-value datastore
An open-source graph database
Web-based MongoDB admin interface, written with Node.js
Provide support to increase developer productivity in Java
A next-generation graph-relational database
The GitHub/GitLab for database DevSecOps
Open Source Alternative to Vercel, Netlify and Heroku
Cybersecurity protocol for syncing decentralized graph data
Global key-value store in the database
PyMongo - the Official MongoDB Python driver
A scalable, distributed, collaborative, document-graph database
adminMongo is a Web based user interface (GUI) to handle MongoDB
Fast, scalable, robust graph database platform
Open source graph databases are a powerful type of data storage system that allow users to store and manage information in an organized and efficient manner. Graph databases make use of the network-like structure known as “graphs”, that allow for linkages between data points that are easy to visualize. This makes them particularly useful for representing complex relationships between disparate objects or data points.
Unlike traditional relational database management systems where the primary form of interaction is through SQL queries, open source graph databases offer multiple ways to interact with the stored information using standard query languages like Gremlin or SPARQL. Additionally, they also provide greater scalability than other types of databases, allowing organizations to more easily build large knowledge graphs as their needs grow and develop over time. Additionally, because these systems are open source, users have full flexibility in terms of customizing them according to their own specific requirements and preferences.
Finally, one of the major benefits of open source graph databases is that they enable developers to create applications quickly due to increased flexibility when designing software solutions–they allow developers to rapidly prototype and test ideas without having to worry about compatibility with existing structures or interfaces within a framework. Furthermore many such systems also come pre-packaged with additional tools like analytics dashboards which give users more detailed insights into how their data is being used.
Open source graph databases are typically free, making them attractive to many businesses. However, there may be costs associated with setting up, maintaining, and running the database. Depending on the complexity of your project, you could incur fees for developing custom software or a high-performance server system. You may also need to purchase a license for some proprietary software that is used in conjunction with the open source graph databases. Additionally, you may choose to hire a consultant or pay for services like training and support. In addition to these costs, it’s important to factor in the cost of your time spent researching and learning how to use the database correctly as well as any other out-of-pocket expenses related to setting up and running the database. Ultimately, when compared to other database options out there such as private cloud solutions or even traditional relational databases, open source graph databases can provide an economical solution that is accessible and scalable enough for most projects.
Open source graph databases can integrate with a variety of software types, including customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, artificial intelligence (AI) applications, and analytics platforms. Each of these software types has its own strengths when integrated with an open source graph database. For example, CRM systems make it easier to manage customer data by allowing organizations to track customer demographics and interactions. ERP systems facilitate the efficient use of resources by automating time-consuming administrative tasks such as inventory tracking and financial forecasting. AI applications can provide additional insights from large datasets stored in the graph database by detecting patterns which are otherwise undetectable by humans. Analytics platforms give users greater insight into their data by providing streamlined visualizations for current trends or anomalies in the data. By integrating any one of these software types with an open source graph database, a company is able to leverage their existing resources more effectively while gaining valuable new insights into their data that will help them improve their operations and stay ahead of the competition.
First, you will need to decide which specific open source graph database you want to use. Popular options include Neo4j, JanusGraph, OrientDB and ArangoDB. Each of these different databases have unique features that can fulfill your needs; do some research on each as well as read reviews to determine the best fit for your project.
Once you've selected the right database for your requirements, download it from the internet - most open source graph databases are freely available online. Installation instructions should be provided with any downloads; follow those closely and make sure to pay attention to any special prerequisites or compatibility requirements prior to installation.
When you have successfully installed your chosen graph database, you'll need to create a schema - this is basically an outline of what data elements will be stored in the database and how they are related. It's also important at this stage that all necessary permissions are granted, such as who has access rights etc., as well as ensuring that backups/recovery plans are ready if needed. Most modern graph databases feature user-friendly entry points for setting up schemas such as graphical editors or interactive command lines - consult the documentation of your selected product for details on how best to set up yours accordingly.
With everything installed and configured properly, you should now be ready to start using your open source graph database. Your next step should likely be loading some data into it so that it can start performing useful queries or analytics tasks - go through the product’s documentation again so that you understand how data can be added and updated within the system. Finally - start playing around. Writing code against various APIs may sound intimidating but don't worry: there's lots of readily available sample code out there aimed at helping developers get quickly familiarized with their chosen technology stack including graph databases like Neo4J etc.