The Data T

What does it take to build a company that doesn't just survive platform shifts—but compounds through them?

In this episode, Coalesce CEO Armon Petrossian and CPTO Satish Jayanti sit down with Braze CEO & Co-Founder Bill Magnuson and SVP of Growth Spencer Burke for a wide-ranging conversation on building durable, data-driven growth across technology eras—from mobile to cloud to agentic AI.

With 15+ years of operating experience, Bill and Spencer share how Braze leaned into fundamental shifts in customer behavior, invested early in data foundations, and resisted short-term hype in favor of long-term value creation.

Topics covered:

• Why mobile was the first major inflection point—and what it teaches us about AI
• The shift from acquisition-first to retention-driven growth
• What durable growth actually looks like at scale
• How AI is changing workflows without replacing fundamentals
• Agentic AI, composability, and what's coming next
• Leadership lessons from scaling a venture-backed startup to IPO

Whether you're a founder, data leader, or executive navigating rapid change, this episode offers grounded, experience-backed perspective on what actually endures.

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/

With 2025 more than halfway over, it’s time to test this year’s bold data forecasts—and look ahead to what’s still coming. On this episode of The Data T, Sigma CEO Mike Palmer joins Coalesce co-founders Armon Petrossian and Satish Jayanthi to unpack where data and analytics are really heading in 2025. From rewriting Sigma’s platform on day 30 to calling time on traditional BI “insights,” Palmer explains why the next wave will be AI-powered, end-user apps built on a radically slimmer tool stack—and why data leaders should double the scope of their impact and bet bigger than ever.

Key Topics

• Palmer’s path from Teach For America to Sigma CEO
• Consolidation in the data industry: from 50–100 analytics tools to ~20
• Why “insights” and text-to-SQL won’t democratize data
• AI’s role in merging BI, apps, and workflow automation
• Advice for data professionals

Resources:

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

Ion Stoica is a professor of computer science at UC Berkeley, Co-Founder and Executive Chairman of Databricks, and a key architect of the Apache Spark project. Most recently, he’s the Co-Founder of Anyscale, which leverages the open source Ray framework developed in-lab to enable scalable AI workloads, much like Spark revolutionized large-scale data processing.

In this episode of The Data T, we chat with Stoica about his illustrious career, how his obsession with solving hard technical problems led him from networking research to peer-to-peer video, Apache Spark, and ultimately Databricks. He recounts turning Spark’s open-source momentum into a successful enterprise business, crediting speed of execution and targeted hiring for the company’s rise and urging founders to move fast and recruit experienced operators early. Stoica warns that tomorrow’s workloads will demand vertically integrated, multi-accelerator systems. Optimistic yet realistic about AI, he sees reliability and “human-in-the-loop” workflows as today’s gating factors and advises data professionals to embrace continuous learning as the industry accelerates.

Hosted by Armon Petrossian and Satish Jayanthi, co-founders of Coalesce.

Key topics:

• The origins of Apache Spark and Databricks
• Commercializing open source projects
• Scaling AI infrastructure complexity
• Advice for data practitioners

Resources:

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

In this episode of The Data T podcast, Armon Petrossian and Satish Jayanthi sit down with Colin Zima, co-founder and CEO of Omni, to talk about what it takes to reinvent business intelligence. Colin shares his journey from Looker’s early days to building Omni, the lessons learned along the way, and his philosophy of ruthless pragmatism when it comes to data modeling, product development, and AI. The conversation dives deep into the evolving BI landscape, the importance of semantics in AI, and how building trust—whether with customers or stakeholders—can be the real engine of innovation. If you're into the future of data platforms, building in public, or just want to hear what happens when two modern data stack founders compare notes, this one's for you.

Main topics:

• Early startup challenges and reflections
• The evolving BI landscape
• The evolution of the Modern Data Stack
• Perspectives on data modeling
• The role of semantic layers in AI
• Hot takes in data analytics
• Collaboration as a key to success
• Building trust in data teams

Resources:

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

In this episode of The Data T podcast, Armon Petrossian and Satish Jayanthi talk with Nicho Mann, founder and CEO of Stratos Consulting, about the emotional and operational challenges facing data professionals today. Nicho introduces the idea of “data therapy” — a way to talk about the mounting pressure, backlog, and burnout that teams are experiencing as AI hype accelerates. With firsthand stories from the biotech and pharmaceutical space, he explains how many companies are overwhelmed, under-resourced, and unsure how to move forward. The conversation digs into how to focus on what matters, where AI is genuinely helping, and why a more agile, empathetic approach to data work is urgently needed.

Main topics:

• “Data therapy” concept
• AI: hype and expectations vs. reality
• Overwhelmed data teams
• Executive pressure to innovate
• Automating manual, repetitive tasks
• Optimizing data frameworks for AI
• The importance of subject matter expertise
• How to stay grounded in fast change

Resources:

About Stratos Consulting: https://stratosconsulting.com/

About Coalesce: https://coalesce.io/about/

The Data T archive: https://coalesce.io/podcast/

Fresh off the announcement of Coalesce acquiring AI-driven data catalog company CastorDoc, now Coalesce Catalog, co-founders Tristan Mayer and Xavier de Boisredon join the Data T podcast to talk about how it all came together, why his marks a major shift in the industry, how the Modern Data Stack is evolving, and what’s next.

The conversation highlights both companies’ shared vision of making data governance simpler, more intuitive, and embedded early in the data lifecycle. The founders break down three categories of AI applications in data—AI-assisted governance, metadata-driven analytics, and enterprise AI use cases—and stress how strong data foundations are essential to enable all of them. Watch the full episode to learn more about the challenges of making data governance accessible and actionable to a broad range of data users, how organizations can solve those challenges by shifting governance left, and more.

Key Topics

• CastorDoc’s founding story
• Shifting data governance left
• Exploring AI use cases in data governance and data catalogs
• Building trust through data strategy
• The democratization of data skills and insights

Resources

➡️ About Coalesce: https://coalesce.io/

➡️ Practical Data Modeling Substack: https://practicaldatamodeling.substac...

➡️ Coalesce podcast archive: https://coalesce.io/podcast/

In this episode of The Data T, Joe Reis returns to discuss his journey since his last appearance on our podcast. We chat about the lasting impact of his book, Fundamentals of Data Engineering, and his latest focus on data modeling. Data modeling is the focus of Joe’s upcoming book, Practical Data Modeling, which he is writing publicly on his Substack.

We also explore broader industry trends, including the hype around AI, the resurgence of data governance, and shifting perspectives on data modeling methodologies. Tune in to hear Joe’s candid takes on the impact of AI-generated code, the new “vibe coding” trend, and the importance of strong communities—both online and in-person—in reshaping professional networking in the data industry.

Key Topics

• The resurgence of data modeling
• AI hype vs. reality
• Data governance comeback
• Community-driven learning
• The shift toward practical data frameworks

Resources

➡️ About Coalesce: https://coalesce.io/

➡️ Practical Data Modeling Substack: https://practicaldatamodeling.substac...

➡️ Coalesce podcast archive: https://coalesce.io/podcast/