The era of AI-driven data migrations is finally here

In the last decade the data engineering landscape has changed dramatically: the rise of cloud warehouses, the emergence of reverse ETL and other specialized  tools, and the adoption of software engineering best practices in data work through frameworks such as dbt. And yet, we're still doing data migrations the same way we did them decades ago. Despite all our advances in data infrastructure, analytics, and AI, we're still asking highly skilled engineers to spend months (often years) manually converting code line by line, praying they don't miss any edge cases that could compromise data integrity.

It's time we admitted something: humans shouldn't be doing data migrations anymore. Not because humans aren't capable – they absolutely are – but because we finally have the technology to do it better, faster, and more reliably with AI.

The problem with human-led migrations

Let's be honest about what happens in most data migrations today:

1. Teams spend weeks or months in planning meetings, mapping dependencies and creating timelines
2. A data engineer (or often, a team of them working with consultants) spends countless hours manually translating SQL code between dialects
3. They discover edge cases and unexpected complexities that balloon the timeline
4. Stakeholders lose confidence as validation reveals discrepancies
5. The project that was supposed to take six months stretches into twelve, then eighteen
6. Both systems run in parallel far longer than planned, driving up costs

The worst part? This isn't just inefficient – it's also risky. Humans are fantastic at understanding business context and solving novel problems, but they're not great at performing repetitive tasks with perfect consistency. Every manual code translation is an opportunity for error, and every error risks data integrity and stakeholder trust.

Of course, a variety of technologies have been introduced to address at least some of these issues. But they come with their own sets of challenges.

The trouble with most non-human translators

Many companies and open-source projects including lark and SQLGlot provide help with translation. These tools rely on a static, grammar-based approach to parsing the code and translating it. While very capable for many applications, this approach falls apart for large-scale migrations. Here’s why:

• Maintenance. Reliance on deterministic parsing and static grammars means that for the translator to work, someone needs to curate and keep grammars for a large variety of systems up-to-date. A small deficiency likely means failure.
• Framework support. Most translators can handle SQL dialects but not frameworks like stored procedures, dbt, and vendor-specific abstractions.
• Multi-language support. Many large-scale data platform migrations involve translations between different languages, not just SQL dialects — especially when dealing with Hadoop and Spark ecosystems and sprocs with embedded procedural languages.
• Refactoring. often legacy constructs are not directly translatable to modern systems or need to be refactored to perform efficiently. Applying high-level refactoring is impossible with the current state of static transpilers.
• Disconnect from data. Static transpilers operate on the code, but don’t take the outputs of that code into account, and hence cannot act on feedback based on the resulting data.
• Scale. It’s not uncommon to give a static transpiler a few examples to work on as a way to test its ability. And its outputs might look good — at first. But the sheer number of edge cases in any major migration will ultimately create a mountain of manual work.

Rethinking migrations for the AI era

The challenges of traditional migrations point to a clear need: tools that can handle massive scale, maintain perfect consistency, and validate results automatically. As it turns out, these are exactly the kinds of tasks AI – or, specifically, Large Language Models do best.

This isn’t just prognosticating. Datafold has invested in AI for migrations because it’s quite obvious people need a better way to do migrations and AI is ripe to deliver it. Our autonomous data migration agent translates code and uses our data diffing technology to validate outputs. It then continues to cycle code through until it reaches parity. We went down this path because AI is so well suited for the challenge. Here’s why:

A clear definition of success

Unlike many data engineering challenges that require nuanced business context, migrations often have a binary success criteria: does the new system produce exactly the same output as the old one? This clear, quantifiable goal makes migrations perfect for AI-driven automation — as long as you can validate the outputs (more on that later).

Repetitive tasks at scale

Modern AI excels at pattern recognition and translation – whether that's between human languages or SQL dialects. While humans tire and lose focus when converting thousands of queries, AI maintains consistency across the entire codebase. No coffee breaks needed.

Self-correcting validation loops

The real power of AI-driven migrations lies in the feedback loop. When a translation isn't perfect, LLMs can immediately incorporate that feedback and adjust, learning from each iteration. No emotional response, no context switching – just continuous improvement until the code achieves parity between legacy and new systems.

Why now is the moment

We're at a unique technological inflection point that makes AI-powered migrations not just possible, but practical.

Previous generations of SQL translators relied on brittle, pre-defined grammars that couldn't handle enterprise-scale complexity. Today's large language models have turned this problem on its head. They don't need pre-defined grammars – they can flexibly interpret and generate code with unprecedented accuracy. Combine this with advanced validation capabilities, and you have a system that can not only translate code but guarantee its correctness.

Let humans work on what matters most

To be clear: Data teams succeed because of human expertise and ingenuity. The real value of data engineers lies in solving complex problems that require business context, strategic thinking, and creative solutions:

• Translating business needs into data models
• Architecting scalable data platforms
• Building trust with stakeholders
• Driving data strategy and innovation

But migrations? They're the perfect candidate for automation. They're bounded problems with clear success criteria, requiring massive amounts of repetitive work that machines handle effortlessly. By letting AI handle migrations, we free up our most valuable resource to focus on work that drives real business value.

The future is already here

Imagine running your next migration in weeks instead of years, with higher accuracy and lower risk. Imagine your valuable data engineers focusing on innovation instead of manual code translation. Imagine not having to maintain parallel systems while you validate data parity.

This isn't a far-off future – it's possible with today’s technology. So, the question isn't whether AI will transform how we handle migrations, but rather: why are we still doing them the old way?

Time to evolve

The data world is moving fast. We've embraced cloud warehouses, modern transformation frameworks, and specialized tools that make our lives easier. It's time we did the same for migrations.

The future of data migrations isn't humans copying and pasting SQL code – it's AI handling the heavy lifting while humans focus on strategy and innovation. The technology is ready. Are you?