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Key features

Data Platform Migrations

The Data Migration Agent delivers guaranteed-outcome migrations with fixed price, timeline, and data parity — over 6x faster than traditional approaches.

Data Knowledge Graph & Lineage

The context layer for reliable AI-assisted data engineering — lineage, business logic, usage, and ontology served via MCP to your coding agents.

Data Quality & CI/CD

Value-level data diffs, monitors, and reconciliation power tools — exposed via MCP so your coding agents can validate their own work.

MCP Integration

Connect your AI coding agent to Datafold and interact with your data through natural language — diffs, lineage, monitors, and more.

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Use cases

Data Platform Migrations

Modernize your data platform in weeks, not years, with AI-powered migration automation and cross-database validation.

AI-Assisted Data Development

Supercharge your coding agents with the Data Knowledge Graph and data quality tools via MCP.

CI/CD Testing & Monitoring

Automatically test, data-diff, and validate every pull request before it reaches production.

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Data Knowledge Graph

Private Beta — The Data Knowledge Graph is currently in private beta. Contact the Datafold team at [email protected] to enable this for your organization.
The Data Knowledge Graph (DKG) automatically collects and unifies all information about your data ecosystem — lineage, business logic, usage statistics, BI connections, git history, and organizational knowledge — and serves it to your AI agents via MCP. Unlike data catalogs that rely on manual curation, the DKG is sourced and maintained by AI, and optimized for consumption by the coding agents of your choice. It spans all your data sources and code bases, creating a comprehensive view of your entire data platform that is inaccessible to any single provider on their own. The DKG powers Datafold’s specialized agents (such as the Data Migration Agent) and supercharges external coding agents (Claude Code, Cursor, Windsurf) by providing the context they need to produce reliable results for any data engineering task.

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Getting started

There are a few ways to get started with your first data diff:
1

Create a data diff

Once you’ve integrated a data connection and code repository, you can run a new in-database or cross-database data diff or explore your data lineage.
2

Create automated monitors

Create monitors to send alerts when data diffs fall outside predefined ranges.
3

Set up CI/CD testing

Get started with deployment testing through our universal (No-Code, API) or dbt integrations.

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Learn more