MetricFlow allows you to define, build, and maintain metrics in code.

Semantica 🧠 — A framework for building semantic layers, context graphs, and decision intelligence systems with explainability and provenance.

Make sense of it all. Semantic data modeling and analytics with a sprinkle of AI. https://totalhack.github.io/zillion/

The universal metrics layer. Compatible with 15+ formats: Cube, MetricFlow, LookML, Omni, BSL, LDM, Cortex, Malloy, OSI, SML, TML, Hex, Rill, Superset

Databao agent is an open-source agent that enables you to chat with your data and receive answers in text, interactive charts, and tables.

Databao Context Engine is an open-source engine that automatically generates a governed semantic context from your databases, BI tools, documents, and spreadsheets. It runs locally in your environment and integrates with any LLM to deliver accurate, context-aware answers

Testing Boring SL with DuckDB

API-first semantic engine and query planner for AI agents that compiles declarative YAML models into optimized, dialect-specific SQL across BigQuery, PostgreSQL, Snowflake, ClickHouse, Dremio, Databricks, and DuckDB.

Transform JSON output from Senzing SDK for use with graph technologies, semantics, and downstream LLM integration

This repo contains an end-to-end analytics engineering project within the marketing domain. Utilisation of DBT semantic layer is introduced in this project to standardize metrics definition that will be used later to produce end data products.

Ontology-based MCP server that analyzes database schemas (PostgreSQL, Snowflake, ClickHouse, Dremio) and generates RDF/OWL ontologies with SQL mappings for fan-trap-free Text-to-SQL.

QueryWise: Natural-language SQL assistant with semantic context, glossary- aware query generation, and a React/FastAPI stack.

A Model Context Protocol (MCP) server that provides MetricFlow CLI tools

A proof-of-concept Flask app that converts natural language to SQL queries based a simple semantic model.

MCP agent for semantic metrics governance with built-in trust scoring, lineage visualization, and conversational metric definition. Designed for data teams working with dbt, LookML, and modern semantic layers.

The Reference Implementation for EU AI Act (Article 10). Cryptographic semantic binding to ensure deterministic integrity for High-Risk AI. (NEN/ISO JTC 25 Aligned)

Full-Funnel AI Marketing Analytics. A modern data stack powered by dbt MetricFlow and MCP. Natural language insights across Google/Meta Ads, CRM, and 5 data warehouses. Includes XGBoost lead scoring and a $0/mo architecture.

Full-stack analytics pipeline: 400M+ e-commerce events through dbt Core, BigQuery, Dagster, and Omni Analytics. RFM segmentation, conversion funnels, churn analysis. Infrastructure as code, CI/CD, git-backed semantic layer.

Automated semantic modeling platform for generating consistent analytics-ready semantic layers using Delta Lake metadata and Python.