Skip to main content

DataBridge AI Pro - Advanced data reconciliation, AI agents, and enterprise features

Project description

DataBridge AI Pro

Enterprise-grade data reconciliation, AI agents, and advanced analytics — ~297 tools.


Overview

DataBridge AI Pro extends the Community Edition (~106 tools) with 19 additional modules and ~191 tools for enterprise data management. Pro includes everything in Community Edition plus:

Data Infrastructure

Module Tools Description
Hierarchy Builder 49 Multi-level hierarchy projects (up to 15 levels) for financial reporting and organizational structures
Wright Pipeline 31 4-object data mart factory (VW_1 → DT_2 → DT_3A → DT_3) with hierarchy integration
Cortex AI 26 Snowflake Cortex integration — natural language to SQL, AI reasoning loops, semantic models
Data Catalog 19 Centralized metadata registry with business glossary and automatic lineage detection
Faux Objects 18 Domain persona-based hierarchy generation and semantic modeling
Connections 16 Multi-database connectivity management for Snowflake, PostgreSQL, MySQL, and more
Hierarchy-Graph Bridge 5 Event-driven sync between hierarchies, GraphRAG vector store, and lineage graph

AI & Automation

Module Tools Description
AI Orchestrator 16 Multi-agent task coordination, event publishing, and workflow management
PlannerAgent 11 AI-powered workflow planning, agent suggestions, and execution optimization
GraphRAG Engine 10 Anti-hallucination layer with graph + vector retrieval-augmented generation
Unified AI Agent 10 Cross-system operations with Book/Librarian/Researcher pattern
Smart Recommendations 5 Context-aware feature suggestions and guided workflows

Governance & Operations

Module Tools Description
Data Observability 15 Real-time metrics, alerting, anomaly detection, and health scoring
Data Versioning 12 Semantic versioning, snapshots, rollback, and diff for all data objects
Git/CI-CD 12 Automated git workflows, GitHub PR creation, and CI/CD pipeline generation
Lineage Tracking 11 Column-level lineage from SQL/dbt with impact analysis
Console Dashboard 5 Real-time broadcast messaging and system monitoring
Schema Matcher 5 Cross-database schema comparison and fuzzy column mapping
Data Matcher 4 Row-level data comparison across database connections

Requirements

  • DataBridge AI Community Edition >= 0.40.0
  • Valid Pro or Enterprise license key
  • Python 3.10+

Installation

Step 1: Set Your License Key

# Set environment variable
export DATABRIDGE_LICENSE_KEY="DB-PRO-YOURCOMPANY-20260101-yoursignature"

# Or add to .env file
echo 'DATABRIDGE_LICENSE_KEY=DB-PRO-YOURCOMPANY-20260101-yoursignature' >> .env

Step 2: Install from GitHub Packages

# Install Pro package
pip install databridge-ai-pro --extra-index-url https://ghp_TOKEN@raw.githubusercontent.com/datanexum/DATABRIDGE_AI/main/

Step 3: Verify Installation

from databridge_ai_pro import get_pro_status

status = get_pro_status()
print(f"License valid: {status['license_valid']}")
print(f"Features: {status['features']}")

Pro Examples Add-on

The Pro Examples package (databridge-ai-examples) provides comprehensive tests and tutorials:

Category Contents Count
Beginner Use Cases Pizza, friends, school, sports tutorials 4 cases
Financial Use Cases SEC EDGAR, Apple, Microsoft analysis 7 cases
Faux Objects Use Cases Domain persona tutorials 8 cases
CE Test Suite Data loading, hashing, fuzzy, dbt, quality, diff ~12 files
Pro Test Suite Hierarchy, cortex, catalog, versioning, wright ~15 files
Shared Fixtures conftest.py, sample data 2 files
# Install CE tests + beginner tutorials
pip install databridge-ai-examples

# Install with Pro tests + advanced tutorials (requires Pro key)
pip install databridge-ai-examples[pro]

Feature Highlights

Cortex AI Agent

AI-powered data analysis using Snowflake Cortex:

# Via MCP tools
cortex_complete(prompt="Analyze sales trends", model="mistral-large")
cortex_reason(question="Why did revenue drop in Q3?", max_steps=5)

# Cortex Analyst — natural language to SQL
analyst_ask(question="What was total revenue by region?",
            semantic_model_file="@ANALYTICS.PUBLIC.MODELS/sales.yaml")

Hierarchy Builder

Multi-level hierarchy management for financial reporting:

# Create and manage hierarchies
create_hierarchy_project(name="Revenue P&L", description="Revenue hierarchy")
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="...")

Wright Pipeline

Generate complete data mart structures, with direct hierarchy integration:

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

# Generate the full 4-object pipeline
generate_mart_pipeline(config_name="upstream_gross")

# Generate Wright pipeline directly from a hierarchy project
wright_from_hierarchy(project_id="revenue-pl", report_type="GROSS")

# Sync Wright mart config when hierarchy changes
wright_hierarchy_sync(config_name="upstream_gross", project_id="revenue-pl")

Hierarchy-Graph Bridge

Event-driven sync between hierarchies and downstream subsystems:

# Check bridge sync status
hierarchy_graph_status(project_id="revenue-pl")

# Reindex hierarchy into vector store for RAG search
hierarchy_reindex(project_id="revenue-pl")

# Build lineage graph from hierarchy relationships
hierarchy_lineage_build(project_id="revenue-pl")

# Search hierarchies via RAG-powered vector index
hierarchy_rag_search(query="Which hierarchies map to ACCOUNT_CODE?", top_k=5)

# Analyze downstream impact of hierarchy changes
hierarchy_impact_analysis(project_id="revenue-pl", node_id="h-42")

GraphRAG Engine

Validate AI outputs against your data:

# Search with context
results = rag_search(query="revenue by region", top_k=5)

# Validate AI-generated content
validation = rag_validate_output(content="Revenue increased 20%", sources=results)

Data Observability

Monitor data quality in real-time:

# Record metrics
obs_record_metric(name="hierarchy.validation.success_rate", value=98.5,
                  type="gauge", tags='{"project_id": "revenue-pl"}')

# Create alert rules
obs_create_alert_rule(name="row_count_drop",
                      metric_name="row_count", threshold=900000,
                      comparison="<", severity="critical")

# Get asset health
obs_get_asset_health(asset_id="revenue-pl", asset_type="hierarchy_project")

Data Catalog

Comprehensive metadata management:

# Scan a connection for metadata
catalog_scan_connection(connection_id="snowflake_prod")

# Search the catalog
results = catalog_search(query="customer dimension")

# Get automatic lineage from SQL
lineage = catalog_auto_lineage_from_sql(sql="SELECT * FROM dim_customer")

Lineage Tracking

Column-level lineage and impact analysis:

# Track lineage from SQL
catalog_auto_lineage_from_sql(sql="INSERT INTO fact_sales SELECT ...")

# Analyze change impact
catalog_impact_from_asset(asset_id="dim_customer")

License Tiers

Feature Community Pro Pro Examples Enterprise
Data Reconciliation (~106 tools)
Hierarchy Builder (49 tools)
Wright Pipeline (31 tools)
Cortex AI Agent (26 tools)
Data Catalog (19 tools)
Faux Objects (18 tools)
Connections (16 tools)
AI Orchestrator (16 tools)
Data Observability (15 tools)
Data Versioning (12 tools)
Git/CI-CD (12 tools)
Lineage Tracking (11 tools)
PlannerAgent (11 tools)
GraphRAG Engine (10 tools)
Unified AI Agent (10 tools)
Hierarchy-Graph Bridge (5 tools)
Console Dashboard (5 tools)
Schema Matcher (5 tools)
Data Matcher (4 tools)
47 Tests + 19 Tutorials
Custom Agents
White-label
SLA Support
On-premise Deploy

License Key Format: DB-{TIER}-{CUSTOMER_ID}-{EXPIRY}-{SIGNATURE}

Package Distribution

Package Location Install
databridge-ai PyPI (public) pip install databridge-ai
databridge-ai-pro GitHub Packages (private) pip install databridge-ai-pro (+ license key)
databridge-ai-examples GitHub Packages (private) pip install databridge-ai-examples (+ license key)

Support

Contact

License

Proprietary - see LICENSE for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

databridge_ai_pro-0.44.0.tar.gz (12.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

databridge_ai_pro-0.44.0-py3-none-any.whl (15.9 kB view details)

Uploaded Python 3

File details

Details for the file databridge_ai_pro-0.44.0.tar.gz.

File metadata

  • Download URL: databridge_ai_pro-0.44.0.tar.gz
  • Upload date:
  • Size: 12.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for databridge_ai_pro-0.44.0.tar.gz
Algorithm Hash digest
SHA256 6680dac28f1c5f68937d67ef9c5fb0f46c5c8613532684095cde24b3bc0a3a4a
MD5 b0ff7ab1e606090f524fe240a1fec26f
BLAKE2b-256 6efe00d0b6dc7a4d116048c76f24a41d52a3192c59ec78dd87b48c7217a819d3

See more details on using hashes here.

File details

Details for the file databridge_ai_pro-0.44.0-py3-none-any.whl.

File metadata

File hashes

Hashes for databridge_ai_pro-0.44.0-py3-none-any.whl
Algorithm Hash digest
SHA256 af6840f1988b291c9989964fbcf312a1af942639295b826c96f3e8a8485192d8
MD5 2c9471ab3a67cf9c77aa5d5c804eceae
BLAKE2b-256 4c6f3245fbd89c2ca8bf551ba448aebf67f0fc5c2320d669db8ff5f55199168e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page