databridge-ai 0.34.0

DataBridge AI - An open-source, MCP-native data reconciliation engine with tools for hierarchy management, data quality, and analytics

DataBridge AI

DataBridge AI is a headless, MCP-native data reconciliation engine with 292 tools for hierarchy management, data quality, and analytics.

Features

Module	Tools	Description
Data Reconciliation	38	Compare and validate data from CSV, SQL, PDF, JSON sources
Hierarchy Builder	44	Create and manage multi-level hierarchy projects (up to 15 levels)
Cortex AI	25	Snowflake Cortex AI with natural language to SQL
Wright Module	18	Hierarchy-driven data mart generation with 4-object pipeline
Data Catalog	15	Centralized metadata registry with business glossary
Data Versioning	12	Semantic versioning, snapshots, rollback, and diff
Lineage Tracking	11	Column-level lineage and impact analysis
Git/CI-CD	12	Automated workflows and GitHub integration
dbt Integration	8	Generate dbt projects from hierarchies
Data Quality	7	Expectation suites and data contracts
Templates & Skills	16	Pre-built templates and AI expertise definitions
AI Orchestrator	27	Multi-agent coordination with task queues
And more...	54	Diff utilities, recommendations, console, etc.

Installation

# Basic installation
pip install databridge-ai

# With PDF support
pip install databridge-ai[pdf]

# With Snowflake support
pip install databridge-ai[snowflake]

# Full installation
pip install databridge-ai[all]

Quick Start

As MCP Server (Claude Desktop)

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "DataBridge_AI": {
      "command": "python",
      "args": ["-m", "src.server"]
    }
  }
}

Web UI Dashboard

cd databridge-ce/ui
python server.py
# Open http://127.0.0.1:5050

Programmatic Usage

from src.server import mcp

# Run as MCP server
mcp.run()

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Claude / LLM Client                       │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                      MCP Protocol                            │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                   DataBridge MCP Server                      │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │  Hierarchy  │  │   Cortex    │  │   Wright    │         │
│  │   Builder   │  │   Agent     │  │   Module    │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐         │
│  │    Data     │  │   Lineage   │  │    Data     │         │
│  │   Catalog   │  │   Tracker   │  │   Quality   │         │
│  └─────────────┘  └─────────────┘  └─────────────┘         │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│              Snowflake / CSV / SQL / PDF                     │
└─────────────────────────────────────────────────────────────┘

Core Modules

Hierarchy Builder

Create and manage multi-level hierarchy projects for financial reporting, organizational structures, and data classification.

# Create a hierarchy project
create_hierarchy_project(name="Revenue P&L", description="Revenue hierarchy")

# Add hierarchies with source mappings
create_hierarchy(project_id="...", name="Product Revenue", parent_id="...")
add_source_mapping(hierarchy_id="...", source_column="ACCOUNT_CODE", source_uid="41%")

# Export and deploy
export_hierarchy_csv(project_id="...")
generate_hierarchy_scripts(project_id="...")

Cortex AI Integration

Natural language to SQL and AI-powered data operations using Snowflake Cortex.

# Configure Cortex
configure_cortex_agent(connection_id="snowflake-prod", cortex_model="mistral-large")

# Natural language query
analyst_ask(question="What was total revenue by region last quarter?",
            semantic_model_file="@ANALYTICS.PUBLIC.MODELS/sales.yaml")

# AI reasoning loop
cortex_reason(goal="Analyze data quality in PRODUCTS table")

Wright Module (Data Mart Factory)

Generate data marts using the 4-object pipeline pattern: VW_1 → DT_2 → DT_3A → DT_3

# Create mart configuration
create_mart_config(project_name="upstream_gross", report_type="GROSS",
                   hierarchy_table="TBL_0_GROSS_LOS_REPORT_HIERARCHY")

# Generate pipeline
generate_mart_pipeline(config_name="upstream_gross")

Data Versioning

Track changes to hierarchies, catalog assets, and semantic models with semantic versioning.

# Create version snapshot
version_create(object_type="hierarchy", object_id="revenue-pl",
               description="Added new cost centers", bump="minor")

# Compare versions
version_diff(object_type="hierarchy", object_id="revenue-pl",
             from_version="1.0.0", to_version="1.1.0")

# Rollback
version_rollback(object_type="hierarchy", object_id="revenue-pl", to_version="1.0.0")

Configuration

Create a .env file:

# Data directory
DATA_DIR=./data

# NestJS backend (optional)
NESTJS_BACKEND_URL=http://localhost:8001
NESTJS_API_KEY=your-api-key

# Snowflake (optional)
SNOWFLAKE_ACCOUNT=your-account
SNOWFLAKE_USER=your-user
SNOWFLAKE_PASSWORD=your-password

# Cortex AI
CORTEX_DEFAULT_MODEL=mistral-large

Documentation

• CLAUDE.md - Complete tool reference and usage guide
• docs/MANIFEST.md - Auto-generated tool manifest
• Wiki - Architecture, getting started, and tutorials

Community Edition

The databridge-ce/ folder contains the open-source Community Edition with:

• Plugin architecture for custom tools
• Web UI dashboard
• Starter templates

Contributing

Contributions are welcome! Please read our contributing guidelines before submitting PRs.

License

MIT License - See LICENSE for details.

Copyright (c) 2024-2026 DataBridge AI Team