DataBridge Core

Your finance team just spent 4 hours on VLOOKUP. This takes 5 seconds.

DataBridge Core is a Python toolkit for data reconciliation, profiling, ingestion, and Excel triage. Compare CSV files, find fuzzy matches, detect schema drift, scan Excel workbooks, and send results to Slack -- from the command line or Python.

pip install databridge-core

5-Second Demo

# Profile a file
databridge profile sales.csv

# Compare two sources -- find orphans, conflicts, match rate
databridge compare source.csv target.csv --keys id

# Fuzzy match names across systems
databridge fuzzy erp_accounts.csv gl_accounts.csv --column name --threshold 80

# Scan Excel files and classify by archetype
pip install 'databridge-core[triage]'
databridge triage ./excel_files/

Python API

from databridge_core import compare_hashes, profile_data, load_csv

# Profile your data
profile = profile_data("chart_of_accounts.csv")
print(f"{profile['rows']} rows, {profile['columns']} columns")
print(f"Potential keys: {profile['potential_key_columns']}")

# Compare two sources
result = compare_hashes("source.csv", "target.csv", key_columns="account_id")
stats = result["statistics"]
print(f"Match rate: {stats['match_rate_percent']}%")
print(f"Conflicts: {stats['conflicts']}, Orphans: {stats['total_orphans']}")

Templates

from databridge_core.templates import TemplateService

svc = TemplateService(templates_dir="templates")
templates = svc.list_templates(domain="accounting")
rec = svc.get_template_recommendations(industry="manufacturing", statement_type="pl")

Slack Integration

from databridge_core.integrations import SlackClient

slack = SlackClient(bot_token="xoxb-...")
slack.send_message("#data-ops", "Reconciliation complete: 99.5% match rate")
slack.post_reconciliation_report("#data-ops", result)

Excel Triage

from databridge_core.triage import scan_and_classify

result = scan_and_classify("./excel_files/", output_dir="./reports/")
print(f"Scanned {result['summary']['total_files']} files")
print(f"Archetypes: {result['summary']['archetype_counts']}")

Commands

Optional Extras

pip install 'databridge-core[fuzzy]'    # Fuzzy matching (rapidfuzz)
pip install 'databridge-core[pdf]'      # PDF text extraction (pypdf)
pip install 'databridge-core[ocr]'      # OCR image extraction (pytesseract)
pip install 'databridge-core[sql]'      # Database queries (sqlalchemy)
pip install 'databridge-core[triage]'   # Excel triage scanning (openpyxl)
pip install 'databridge-core[all]'      # Everything
pip install 'databridge-core[dev]'      # Development tools (pytest, ruff, build)

Modules

Built for Finance

DataBridge Core is the open-source foundation of DataBridge AI -- a full platform for financial hierarchy management, dbt model generation, and enterprise data reconciliation with 268 MCP tools.

How it works: Upload your Chart of Accounts. Get a production-ready financial hierarchy and dbt models. Zero config.

What's Next?

DataBridge Core provides the SDK foundation. For the full platform experience:

• MCP Server (268 tools): pip install databridge-ai -- headless AI-native data engine
• Docker: docker run -p 786:786 ghcr.io/datanexum/databridge-mcp:latest
• Claude Code Plugin: claude plugin install datanexum/databridge-plugin

See the full documentation for details.

Changelog

See CHANGELOG.md for full version history.

License

MIT