The Future of Data Engineering: AI, LLMs, and Automation

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Feb 26, 2025

Gleb Mezhanskiy, CEO and co-founder of Datafold, shares insights from his journey in data engineering and the integration of AI. He discusses how large language models can streamline code writing, improve data accessibility, and facilitate testing and code reviews. Mezhanskiy emphasizes the challenges at the intersection of AI and data workflows, advocating for continuous adaptation. With practical applications like text-to-SQL and enhanced data observability, he paints an optimistic picture for the future of data engineering.

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ANECDOTE

Gleb's Data Engineering Background

Gleb Mezhanskiy built three data platforms from scratch at different companies, including Autodesk and Lyft.
This experience exposed him to the manual, error-prone nature of data engineering workflows.

INSIGHT

AI in Data Engineering: Beyond the Hype

Simply applying AI to data engineering doesn't magically solve all problems.
LLMs have potential, but realizing their capabilities requires effort and understanding.

INSIGHT

Data Engineering vs. Software Engineering

Data engineering and software engineering are related but distinct, especially concerning AI integration.
Data engineers need specialized AI tools that understand data context, unlike software engineering tools.

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Summary
In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large language models (LLMs) to enhance productivity and reduce manual toil. The conversation covers the potential of AI to transform data engineering tasks, such as text-to-SQL interfaces and creating semantic graphs to improve data accessibility, and explores practical applications of LLMs in automating code reviews, testing, and understanding data lineage.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.
Your host is Tobias Macey and today I'm interviewing Gleb Mezhanskiy about

Interview

Introduction
How did you get involved in the area of data management?
modern data stack is dead
where is AI in the data stack?
"buy our tool to ship AI"
opportunities for LLM in DE workflow

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.

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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA