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For agents: Data engineering automation platform with AI agents, data diff, data quality tools, a Data Migration Agent, and an MCP server for connecting AI agents to Datafo

data diffdata migrationdata qualitymcp serverdata engineering

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AI and machine learning · Data and analytics · AI agents and MCP
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# Datafold

- [Datafold](https://docs.datafold.com/welcome.md): Datafold is the data engineering automation platform that combines specialized AI agents with a context layer and data quality tools — so data teams and their coding agents ship higher-quality data faster, migrate with confidence, and optimize platform costs.
- [MCP](https://docs.datafold.com/datafold-mcp.md): Connect your AI agent to Datafold and interact with your data through natural language
- [Migration Automation](https://docs.datafold.com/data-migration-automation/datafold-migration-automation.md): Modernize your data platform in weeks, not years. Datafold's Data Migration Agent delivers guaranteed-outcome migrations with fixed price, timeline, and data parity — over 6x faster and cheaper than traditional approaches.
- [Datafold Migration Agent](https://docs.datafold.com/data-migration-automation/datafold-migration-agent.md): The Data Migration Agent delivers guaranteed-outcome migrations with fixed price, timeline, and data parity — over 6x faster than traditional approaches.
- [What's a Data Diff?](https://docs.datafold.com/data-diff/what-is-data-diff.md): A data diff is the value-level comparison between two tables, used to identify critical changes to your data and guarantee data quality.
- [How Datafold Diffs Data](https://docs.datafold.com/data-diff/how-datafold-diffs-data.md): Data diffs allow you to perform value-level comparisons between any two datasets within the same database, across different databases, or even between files.
- [Creating a New Data Diff](https://docs.datafold.com/data-diff/in-database-diffing/creating-a-new-data-diff.md): Setting up a new data diff in Datafold is straightforward.
- [Results](https://docs.datafold.com/data-diff/in-database-diffing/results.md): Once your data diff is complete, Datafold provides a concise, high-level summary of the detected changes in the Overview tab
- [Best Practices](https://docs.datafold.com/data-diff/in-database-diffing/best-practices.md): We share best practices that will help you get the most accurate and efficient results from your data diffs.
- [Creating a New Data Diff](https://docs.datafold.com/data-diff/cross-database-diffing/creating-a-new-data-diff.md): Datafold's Data Diff can compare data across databases (e.g., PostgreSQL <> Snowflake, or between two SQL Server instances) to validate migrations, meet regulatory and compliance requirements, or ensure data is flowing successfully from source to target.
- [Results](https://docs.datafold.com/data-diff/cross-database-diffing/results.md): Once your data diff is complete, Datafold provides a concise, high-level summary of the detected changes in the Overview tab.
- [Best Practices](https://docs.datafold.com/data-diff/cross-database-diffing/best-practices.md): When dealing with large datasets, it's crucial to approach diffing with specific optimization strategies in mind. We share best practices that will help you get the most accurate and efficient results
…

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