Awesome GitHub RepositoriesSemantic Data Models

This project is a business intelligence suite and SQL data visualization platform used for data analysis, reporting, and monitoring. It provides a web application for exploring datasets and building interactive dashboards, complemented by a web-based SQL query editor for analyzing raw data from connected stores. The platform features a semantic data layer to define standardized metrics and dimensions, ensuring consistent data interpretation across reports. It includes a security framework with role-based access control to manage user permissions and authentication across shared dashboards. T

Metabase is a business intelligence platform designed to connect to various storage systems and relational databases for data exploration, visualization, and reporting. It provides a centralized environment where users can build queries through a graphical interface or raw code, transforming raw information into interactive dashboards and charts. The platform is built to support self-service analytics, allowing non-technical team members to extract insights without requiring deep knowledge of database syntax. The platform distinguishes itself through a metadata-driven modeling layer that abst

This project is a Python-based framework that functions as a generative AI agent for programmatic data analysis. It enables users to interact with structured data sources through natural language prompts, translating these requests into executable code to perform analysis, data cleaning, and visualization. By maintaining conversational context across multi-turn interactions, the system allows for iterative exploration and the building of complex data narratives. The framework distinguishes itself through a robust semantic layer and secure execution model. It maps raw datasets to descriptive m

Cube is a semantic data layer that provides a unified framework for defining business metrics, dimensions, and relationships across diverse data sources. By acting as a headless business intelligence engine, it transforms raw data into a governed model that can be queried via SQL, REST, and GraphQL interfaces. This architecture ensures consistent data definitions and logic across all downstream analytical applications and reporting tools. The platform distinguishes itself through its integrated conversational AI capabilities, which allow users to explore data using natural language. It orches

Cube is a semantic layer data platform that maps raw SQL databases to standardized business metrics and dimensions. It functions as a SQL dialect translator, converting abstract semantic queries into optimized SQL statements for various cloud data warehouses. The platform operates as a multi-tenant data gateway, isolating information and security permissions for different customers within a single deployment. It includes a relational caching engine that stores pre-aggregated query results to reduce latency and decrease the load on primary data warehouses. The system provides a REST-based int

Gensim is a natural language processing toolkit designed for large-scale text analysis and the training of semantic vector embeddings. It provides a framework for identifying latent thematic structures within document collections and calculating semantic similarity between text segments using unsupervised statistical algorithms. The project is distinguished by its ability to handle datasets that exceed available system memory through incremental corpus streaming, which processes documents one at a time from disk. It utilizes sparse vector representations and dictionary-based token mapping to

WrenAI is a platform designed to enable natural language interaction with relational and analytical databases. By combining a text-to-SQL engine with semantic data modeling, it allows users to explore structured data through plain language questions, removing the requirement for manual code generation. The system functions by grounding natural language requests in a predefined business logic layer rather than raw database schemas. This semantic approach, supported by context-aware prompt engineering, ensures that generated queries remain consistent and accurate across an organization. The pla

dbt-core is a command-line framework for transforming data within a warehouse using modular SQL and version control. It functions as a data transformation engine that enables users to define data structures and business logic through declarative configuration files, which the system then compiles into executable code. By managing complex data dependencies through a directed acyclic graph, it ensures that transformation tasks execute in the correct order while maintaining a manifest-driven state to track lineage and execution history. The project distinguishes itself through an adapter-based d

API Platform is a headless content management system engine and framework used to build REST and GraphQL APIs. It utilizes schema-driven generation to automatically produce web endpoints based on predefined data model definitions. The platform focuses on semantic data modeling, using structured classes and ontologies to ensure information is organized for search engines and semantic web tools. It provides an automated OpenAPI specification generator and a declarative system for creating administration dashboards to manage data records without custom frontend code. The system includes capabil

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

Supersonic est une plateforme d'analyse de données basée sur LLM et un moteur de couche sémantique qui traduit des questions en langage naturel en requêtes SQL exécutables. Elle fonctionne comme un tableau de bord de business intelligence et une interface texte-vers-SQL, permettant aux utilisateurs de récupérer des métriques métier et des insights via une interface conversationnelle. Le système découple les définitions métier des schémas de base de données physiques en utilisant une couche logique gouvernée pour définir des métriques et des dimensions unifiées. Cette modélisation sémantique permet à la plateforme de mapper des modèles de langage humain vers des modèles sélectionnés et de traduire des déclarations sémantiques abstraites en SQL physique adapté à des moteurs de base de données spécifiques. La plateforme fournit une passerelle de données d'entreprise dotée d'un contrôle d'accès granulaire et basé sur les rôles aux niveaux du jeu de données, de la colonne et de la ligne. Ses capacités incluent la gestion de dialogues multi-tours, la connectivité multi-bases de données et une architecture de plugins pour l'intégration d'outils tiers. Le projet expose ses fonctionnalités via une API programmatique headless et une API de couche sémantique pour la consommation de données externe.

Erupt is a framework for building administrative interfaces, business intelligence layers, and visual workflow engines. It provides a multi-tenant admin panel and an LLM admin framework that automatically generates web-based management consoles and REST endpoints from backend class definitions. The project distinguishes itself by integrating AI agent orchestration, allowing administrators to manage server operations and execute backend logic through a conversational chat interface. It also features a BI semantic layer that maps raw warehouse data into business-oriented cubes for self-service