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A large-scale entity and relation database supporting aggregation of properties
The main features of gchq/gaffer are: Graph Databases, Relational Databases.
Projects with overlapping indexed features include: kakao/actionbase — One database for likes, views, follows — pre-computed, served in real-time. cayleygraph/cayley — Cayley is a graph database engine designed for storing and querying interconnected data using a quad-based data model.… apache/incubator-druid — Apache Druid is a real-time OLAP database and distributed analytics engine. It functions as a columnar time-series… biokoda/actordb — ActorDB distributed SQL database. bitnine-oss/agensgraph — AgensGraph, a transactional graph database based on PostgreSQL. arangodb/arangodb — This project is a multi-model database system designed to store and manage information as documents, graphs, and…
One database for likes, views, follows — pre-computed, served in real-time
Cayley is a graph database engine designed for storing and querying interconnected data using a quad-based data model. It functions as an RDF quad store, managing information through subjects, predicates, objects, and labels. The system features a modular graph store architecture with pluggable backends, allowing it to swap between in-memory storage and various external persistent databases. It includes a GraphQL-inspired API and a dedicated data visualizer for the interactive exploration of nodes and edges. Query capabilities cover bidirectional path traversal and multi-syntax execution usi
Apache Druid is a real-time OLAP database and distributed analytics engine. It functions as a columnar time-series database designed for high-performance analytical queries and the real-time ingestion of streaming and batch datasets. The system provides a framework for high-concurrency analytics, allowing multiple simultaneous users to execute SQL and native queries across large-scale data. It supports mixed data ingestion, combining real-time streaming and batch loading into a single system for unified analysis. The platform includes capabilities for distributed cluster management, enabling
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