Data migrations have long been the costly, painful bottleneck of modernization, dragging on for years and carrying multi-million-dollar price tags. But AI is already flipping that script.

Gleb Mezhanskiy, founder & CEO of Datafold, who has been deep in the trenches of AI-driven migrations, joins us on the Data T podcast to unpack how AI automation is finally making it realistic to move off entrenched legacy ETL tools. Drawing on his years as a hands-on data engineer and PM (including leading a massive migration initiative at Lyft), Gleb explains where migrations go off the rails: millions of lines of legacy code and lack of automation lead to massive human time sunk into reconciliation and QA. He shares how Datafold attacks the problem end-to-end, using agents and data diffing to translate and validate at scale, so teams can “lift and shift” quickly, then refactor with confidence on modern stacks like Snowflake and Coalesce.

Beyond migrations, Gleb and podcast host, Coalesce co-founder and CEO Armon Petrossian, dig into how AI is reshaping data engineering itself. They argue AI won’t replace great engineers; it elevates them, shifting work from tedious rewrites to higher-leverage design, governance, and outcomes. The teams that master AI-assisted workflows, evaluation, and modern patterns will widen the gap, moving from weeks of manual effort to rapid, continuous delivery.

Key Topics

• Data migration challenges
• The role of AI in data migrations
• Limitations of generic AI tools and LLM models for migration projects
• How AI is reshaping data engineering roles
• Predictions for data infrastructure
• AI for productivity

This month on The Data T, we sit down with Erik Duffield, CEO and co-founder of Hakkoda, now an IBM Company, to unpack the latest hot topic in our industry: AI-ready data. Everyone’s talking about it, but what does it really take to build an AI-ready data team or data practice?

AI-readiness isn’t about the latest tool, it’s about a mindset shift. Duffield shares why data programs must now be designed for machines as primary consumers, how data governance has evolved from a blocker to an enabler, and why the speed of iteration, not a single big launch, is the real measure of success. Duffield touches on agentic AI in production, the future of data careers, and what 2026 may hold for both the AI market and enterprise adoption.

Co-hosted by Coalesce cofounders Armon Petrossian and Satish Jayanthi

Key topics:

• What does an AI-ready data program look like?
• Gaps between hype and reality in AI initiatives
• What keeps data leaders up at night?
• Data security concerns
• Data team composition and reskilling for AI success
• Labor force shifts and career progression challenges
• How do you define and measure trusted, AI-ready data?
• Top data trends and predictions for 2006

Resources:

• About Hakkoda, an IBM Company: https://hakkoda.io/
• About Coalesce: https://coalesce.io/

Coalesce is the only data transformation and governance platform designed for the AI era. Built on a metadata-driven framework, Coalesce gives data teams the speed to build and deploy transformations 10× faster—while enforcing the standards, structure, and governance needed to scale sustainably. With Coalesce Catalog, transformation and metadata management come together in a single solution, enabling discovery, trust, and collaboration across the business. Whether accelerating AI-assisted migrations from legacy tools or future-proofing enterprise data architectures, Coalesce provides the guardrails and efficiency to keep data teams AI-ready.

Data governance is far from anyone’s favorite topic, but at Infostrux it’s the superpower behind launching successful AI initiatives. In this episode of the Data T podcast, CEO Pierre Jr Cliche joins Coalesce co-founders Armon Petrossian and Satish Jayanthi to share how Infostrux rides today’s GenAI wave to get buy-in for data governance projects most execs would otherwise overlook: data cataloging, data lineage, and building data transformation frameworks that turn messy data into governed, production-ready assets.

Learn how to build a value narrative that makes data governance feel like a growth and innovation enabler rather than a cost center, why a catalog and clear data roles are essential for AI accuracy, and how to get leadership genuinely excited about investing in “unsexy” data work.

Key Topics

• The shift in data governance perception
• The role of AI in data governance
• Why the majority of Gen AI projects fail
• How to get stakeholders’ buy-in for data governance initiatives
• Top challenges for data governance and the tools to solve them

Resources:

• About Coalesce: https://coalesce.io/about/
• Coalesce podcast archive (The Data T): https://coalesce.io/podcast/