8 repositorios
Database engines that support multiple data models like document, graph, and relational within a single system.
Distinguishing note: Covers the unified storage and querying of diverse data types in one engine.
Explore 8 awesome GitHub repositories matching data & databases · Multi-Model Databases. Refine with filters or upvote what's useful.
Dokploy is a self-hosted platform-as-a-service designed to simplify the deployment and management of containerized applications and databases. It provides a centralized control plane that decouples administrative management from application workloads, allowing users to oversee infrastructure across multiple server nodes through a unified web interface or a command-line tool. The platform distinguishes itself through an extensive library of pre-configured application templates, enabling the rapid deployment of databases, identity providers, and various productivity or development tools. It sup
Functions as a high-performance multi-model database for modern applications.
SurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models. The platform distinguishes itself through its ability to manage agent memory and complex workflows natively. It allows developer
Stores and queries document, graph, relational, and vector data within a single ACID-compliant engine.
Dragonfly is a high-performance, multi-model in-memory data store designed to serve as a drop-in replacement for existing database infrastructures. By utilizing a multi-threaded, shared-nothing architecture and a fiber-based concurrency model, it maximizes CPU utilization and minimizes latency for read and write operations. The system supports a wide range of data structures, including strings, hashes, lists, sets, sorted sets, and JSON documents, while maintaining full compatibility with standard industry wire protocols and client libraries. What distinguishes Dragonfly is its focus on effic
A data storage solution that supports complex structures like hashes, lists, sets, and JSON documents alongside standard key-value pairs.
This project is a comprehensive educational guide and framework for building web scrapers using Python. It provides a course-based approach to data extraction, combining a Python crawler framework with tutorials on web reverse engineering and network traffic analysis. The project distinguishes itself by covering advanced extraction challenges, including the decryption of obfuscated JavaScript and the bypass of anti-scraping measures. It specifically addresses mobile application scraping through the simulation of user interactions and the interception of network traffic. The capability surfac
Supports saving extracted data into diverse models, including relational MySQL tables and document-based MongoDB collections.
This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals
Stores data as documents, graphs, and key-value pairs while supporting unified queries across all models.
immudb is a tamperproof database that maintains an immutable record of entries using cryptographic commit logging. It ensures verifiable database integrity by utilizing Merkle trees to generate membership and consistency proofs that detect unauthorized data alterations. The system employs a multi-model storage engine that unifies key-value, document, and relational data structures within a single immutable backend. It provides compatibility with the PostgreSQL wire protocol, allowing it to integrate with standard SQL clients, ORMs, and database tools. The project covers broad capabilities in
Unifies key-value, document, and relational data structures within a single immutable storage engine.
This project serves as a comprehensive educational repository and technical reference collection, documenting a wide range of software engineering practices and modern development technologies. It provides a structured learning path for developers, curating tutorials and practical examples that cover the full lifecycle of application development, from initial project scaffolding to deployment and maintenance. The repository distinguishes itself by offering deep technical insights into complex architectural patterns, including actor-based concurrency models for managing parallel tasks and cont
Supports diverse data structures like documents and key-value pairs within a single system to simplify storage requirements.
TypeDB es una base de datos de grafos fuertemente tipada y un sistema de gestión de grafos de conocimiento. Sirve como un almacén de datos multimodelo que unifica estructuras relacionales, de documentos y de grafos en un solo entorno, funcionando tanto como una base de datos compatible con ACID como un motor de consultas declarativo. El sistema se distingue por el uso de modelado de hipergrafos n-arios y jerarquías de tipos polimórficos. Emplea un esquema fuertemente tipado para imponer reglas estructurales y validar la integridad de los datos, permitiendo la inferencia polimórfica basada en tipos y el polimorfismo de interfaz basado en roles para resolver relaciones complejas automáticamente durante la ejecución de consultas. La plataforma cubre una amplia gama de capacidades, incluyendo el cálculo de relaciones recursivas mediante tabulación, transacciones con aislamiento de instantáneas y recuperación declarativa de datos. También admite alta disponibilidad mediante replicación de clúster basada en consenso, control de acceso basado en roles e integración con agentes de IA para la recuperación de datos estructurados. La gestión se realiza a través de una interfaz de línea de comandos, y el sistema proporciona herramientas para visualizar esquemas de grafos y auditar la actividad administrativa.
Unifies relational, document, and graph data models into a single, strongly-typed storage environment.