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

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.

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/

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/

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/

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/

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/

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/

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/

The conversation covers the top trends in generative AI, customer engagement, and data infrastructure, with a deep dive into AI Decisioning, the latest innovation from Hightouch that uses continuous experimentation and reinforcement learning to optimize marketing strategies.

Manohar provides insights into how organizations can better leverage their data platforms to activate data for marketing and operations, discusses the challenges in implementing Large Language Models (LLMs), the importance of foundational infrastructure for all AI initiatives, the emerging popularity of open-source table formats like Apache Iceberg, and more.

Key topics include:

• Data predictions for 2025
• AI Decisioning from Hightouch
• Practical use cases for leveraging LLMs
• Generative AI: hype vs reality
• Data infrastructure trends and innovations for 2025
• The future of AI in marketing

Watch the video recording of this episode here.

Resources:

The Top Data Trends for 2025

Learn more about Coalesce

Learn more about Hightouch

Connect with the speakers:

Tejas Manohar

Armon Petrossian

Satish Jayanthi

Hosted by Damien Walker and Glen Dawson, the conversation explores the benefits of shifting from application-centric to data-centric approaches to data management and highlights strategies for overcoming technical and organizational barriers in SAP migrations.

Key topics include:

• Transitioning from application-centric to data-centric models: The shift to modern data platforms like Snowflake highlights the need to rethink data architectures.
• End-to-end data integration: The challenges in unifying SAP and non-SAP data underscores the importance of platforms like Fivetran in data modernization initiatives.
• Navigating SAP end-of-life decisions: Key considerations for organizations that face critical decisions as they approach end-of-life deadlines for certain SAP platforms.
• SAP migrations and cloud adoption: The benefits of moving SAP systems to the cloud and recommendations for addressing the challenges of legacy systems with modern, scalable solutions.

Watch the video recording of this podcast episode here.

Resources:

Power SAP data transformations at scale

Demo: Integrating SAP Data Into SnowflakeWith Fivetran, Coalesce and phData

Coalesce

Snowflake

Fivetran

Connect with the speakers:

David Richert

Edwin Commandeur

Damien Walker

Glen Dawson

• Orchestra’s founding story
• Challenges in data orchestration
• The importance of data governance
• Enabling a successful data mesh implementation
• Navigating a crowded data space
• Is column-level lineage really necessary?
• Inspiration from business leaders, and more.

Watch the video recording of this podcast episode here.

Resources:

• About Orchestra
• About Coalesce
• Coalesce podcast
• Announcing Orchestra's Integration with Coalesce
• Data Orchestration is Evolving

nd. The discussion touches on Cube’s founding story, innovating the semantic layer with a universal approach, and their data management strategy. Listen to the full episode for more on:

• Cube’s founding story: The company’s origins as an open-source project focused on embedding BI tools into Slack, eventually pivoting to creating a universal semantic layer.
• Semantic layer innovation: The importance of a universal semantic layer for ensuring consistent metrics across different BI tools.
• Cube’s latest product features, including next-gen data modeling, AI-powered capabilities, and more.
• Predictions and top industry trends in 2025: AI will play a growing role in data engineering, especially with data modeling and optimizing data queries; consolidation within the Modern Data Stack, the evolution of AI-powered tools in analytics, and more.

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

Resources:

• About Cube: https://cube.dev/
• About Coalesce: https://coalesce.io/
• Article: Achieve Data Maturity with an Integrated Semantic Layer
• News: Coalesce and Cube Partner to Bring a Universal Semantic Layer to Data Pipeline Development

Connect with our hosts:

• Armon Petrossian
• Satish Jayanthi

Connect with this episode's guests:

• Artyom Keydunov
• Pavel Tiunov

In this episode:

• The origin of Fivetran and its mission to simplify data ingestions
• The origin of the Modern Data Stack
• Data accessibility and infrastructure innovation
• Fivetran’s hybrid deployment model
• Scaling management structures as a co-founder
• The future of data lakes and open file formats, and more

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

Topics covered in this episode include the reality of data mesh implementations vs the theoretical concepts, use cases where implementing data mesh makes sense, assembling the right team for a successful transition to a data mesh structure, and more.

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

• the future of spreadsheets and SQL
• addressing the largest bottlenecks in analytics use cases
• the impact of Gen AI and LLMs on BI
• challenges in AI interface design
• the potential of AI for productivity, and more.

Tune in to hear more and bring your questions for our guest and hosts, Coalesce co-founders Armon Petrossian and Satish Jayanthi.

Watch a video recording of this episode: https://coalesce.io/podcast/ep-16-the-10x-data-engineer-with-gu-xie/

Watch recordings of past episodes: https://coalesce.io/podcast/

Hear from our customers, see product demos, and more: https://coalesce.io/videos/

Learn more about Coalesce: https://coalesce.io/

Ten years ago hardly anyone had heard of Snowflake. Just two years ago, LLMs were an obscure acronym known only to those deeply immersed in AI and ML. Today, it seems like AI is everywhere. What will tomorrow bring? What do VCs look for when investing in early-stage startups in the data and AI field. What are some markers of success? What are some red flags? Listen to the podcast episode to find out.

Matthew Tischler, Chief Financial Officer of e-retailer RuffleButts and Michael Tantrum, National Sales Director at Resultant, join Coalesce co-founders Armon Petrossian and Satish Jayanthi to unravel the secrets of effectively communicating with top-level executives, gaining their buy-in, and ultimately ensuring a data team’s efforts are not just understood, but genuinely appreciated and championed by the C-Suite.

• how AI and LLMs will change the Modern Data Stack

• how AI and LLMs will change data engineering

• the future of SQL and knowledge graphs

• Muglia's "you heard it here first" take on Data Mesh

• and much more!

About Coalesce:Founded in 2020, Coalesce is the only data transformation platform built for scale. Coalesce combines the speed of an intuitive graphical user interface (GUI), the flexibility of code, and the efficiency of automation, empowering its customers with increased data team productivity and insights. Based in San Francisco, Calif., Coalesce is backed by Emergence Capital, 11.2 Capital, GreatPoint Ventures, and Industry Ventures and supports customers worldwide. Learn more at Coalesce.io. Get a demo: https://coalesce.io/request-demo/Start free: https://coalesce.io/start-free/

• Top use cases for leveraging Generative AI in data
• The 80-20 approach for integrating Generative AI into your data workflows
• Risks and concerns
• How will Generative AI impact roles and careers within data teams?
• Are analytics jobs at risk?

• What is DataOps and how is it similar to–and different from–DevOps and MLOps?
• What problems does DataOps solve?
• What are the biggest pitfalls of DataOps?
• How can data teams set themselves up for success within the larger organization and bring value to the business?

• Top expectations for data engineering teams in 2023, including how to measure the ROI of the team
• Build vs buy data platforms and solutions: rethinking decisions in today’s economy
• The cost of compute vs the cost of people: which one matters more?
• Will 2023 finally be the year of data quality?
• How are data teams changing in today’s economy
• ChatGPT: will it impact data engineers?

• What are the biggest challenges facing data engineering teams?

• What parts of the analytics workflow is most challenging for organizations?

• What are the characteristics of a great data leader?

• Is today’s economy the catalyst the market needed to evolve and innovate?

• What is a data engineer’s role in AI?

About Coalesce:Founded in 2020, Coalesce is the only data transformation platform built for scale. Coalesce combines the speed of an intuitive graphical user interface (GUI), the flexibility of code, and the efficiency of automation, empowering its customers with increased data team productivity and insights. Based in San Francisco, Calif., Coalesce is backed by Emergence Capital, 11.2 Capital, GreatPoint Ventures, and Industry Ventures and supports customers worldwide. Learn more at Coalesce.io. Get a demo: https://coalesce.io/request-demo/Start free: https://coalesce.io/start-free/

• What are data contracts: key definitions

• Why data and infrastructure problems inspired the idea for data contracts

• How data contracts help enforce data governance

• How data contracts differ from APIs

• When are data contracts most effective

• How data contracts relate to data mesh

• How to effectively apply data contracts to data modeling

• How data contracts can help remove the biggest bottleneck in businesses deriving true value from AI/ML

About Coalesce:Founded in 2020, Coalesce is the only data transformation platform built for scale. Coalesce combines the speed of an intuitive graphical user interface (GUI), the flexibility of code, and the efficiency of automation, empowering its customers with increased data team productivity and insights. Based in San Francisco, Calif., Coalesce is backed by Emergence Capital, 11.2 Capital, GreatPoint Ventures, and Industry Ventures and supports customers worldwide. Learn more at Coalesce.io. Get a demo: https://coalesce.io/request-demo/Start free: https://coalesce.io/start-free/

Watch the full episode to learn:

• What is data modeling
• Why data modeling took a backseat and the dangers of agile
• Why is data modeling making a roaring comeback
• How bad data modeling can drive up your compute costs
• How can companies of various sizes approach data modeling to ensure a successful analytics organization
• Coalesce as a data modeling tool: How Coalesce enables you to visually design, with intent, what you want your data model to be

About Coalesce:

Founded in 2020, Coalesce is the only data transformation platform built for scale. Coalesce combines the speed of an intuitive graphical user interface (GUI), the flexibility of code, and the efficiency of automation, empowering its customers with increased data team productivity and insights. Based in San Francisco, Calif., Coalesce is backed by Emergence Capital, 11.2 Capital, GreatPoint Ventures, and Industry Ventures and supports customers worldwide. Learn more at Coalesce.io.

Get a demo: https://coalesce.io/request-demo/

Start free: https://coalesce.io/start-free/

• Kent’s thoughts on GUI v code approaches and how Coalesce solves the shortcomings of each
• Data governance: How user-defined nodes help data architects implement data standards at scale
• What is “Column awareness”

About Coalesce:

Founded in 2020, Coalesce is the only data transformation platform built for scale. Coalesce combines the speed of an intuitive graphical user interface (GUI), the flexibility of code, and the efficiency of automation, empowering its customers with increased data team productivity and insights. Based in San Francisco, Calif., Coalesce is backed by Emergence Capital, 11.2 Capital, GreatPoint Ventures, and Industry Ventures and supports customers worldwide. Learn more at Coalesce.io.

Get a demo: https://coalesce.io/request-demo/

Start free: https://coalesce.io/start-free/