Adnan Hodzic, Lead Engineer and GenAI Delivery Lead at ING, joined Yuliia how ING successfully scaled generative AI from experimentation to enterprise production. With over 60 GenAI applications now running in production across the bank, Adnan explains ING's pragmatic approach: building internal AI platforms that balance innovation speed with regulatory compliance, treating European banking regulations as features rather than constraints, and fostering a culture where 300+ experiments can safely run while only the best reach production. He discusses the critical role of their Prompt Flow Studio in democratizing AI development, why customer success teams saw immediate productivity gains, how ING structures AI governance without killing innovation, and his perspective on the hype cycle versus real enterprise value. Adnan's blog: https://foolcontrol.org [https://foolcontrol.org/] Adnan's Youtube channel: https://www.youtube.com/AdnanHodzic LinkedIn: https://linkedin.com/in/AdnanHodzic Twitter/X: https://twitter.com/fooctrl

Ryan Dolley, VP of Product Strategy at GoodData and co-host of Super Data Brothers podcast, joined Yuliia and Dumke to discuss the DBT-Fivetran merger and what it signals about the modern data stack's consolidation phase. After 16 years in BI and analytics, Ryan explains why BI adoption has been stuck at 27% for a decade and why simply adding AI chatbots won't solve it. He argues that at large enterprises, purchasing new software is actually the only viable opportunity to change company culture - not because of the features, but because it forces operational pauses and new ways of working. Ryan shares his take that AI will struggle with BI because LLMs are trained to give emotionally satisfying answers rather than accurate ones. Ryan Dolley linkedin [https://www.linkedin.com/in/ryandolley/]

Thomas in't Veld, founder of Tasman Analytics, joined Yuliia and Dumke to discuss why data projects fail: teams obsess over tooling while ignoring proper data modeling and business alignment. Drawing from building analytics for 70-80 companies, Thomas explains why the best data model never changes unless the business changes, and how his team acts as "data therapists" forcing marketing and sales to agree on fundamental definitions. He shares his controversial take that data modeling sits more in analysis than engineering. Another hot take: analytics engineering is merging back into data engineering, and why showing off your DAG at meetups completely misses the point - business understanding is the critical differentiator, not your technology stack.

Elliot Foreman and Andrew DeLave from ProsperOps joined Yuliia and Dumky to discuss automated cloud cost optimization through commitment management. As Google go-to-market director and senior FinOps specialist, they explain how their platform manages over $4 billion in cloud spend by automating reserved instances, committed use discounts, and savings plans across AWS, Azure, and Google Cloud. The conversation covers the psychology behind commitment hesitation, break-even point mathematics for cloud discounts, workload volatility optimization, and why they avoid AI in favor of deterministic algorithms for financial decisions. They share insights on managing complex multi-cloud environments, the human vs automation debate in FinOps, and practical strategies for reducing cloud costs while mitigating commitment risks.

Kasriel Kay, leading data democratization at Velotix, joined Yuliia and Dumke to challenge conventional wisdom about data governance and catalogs. Kasriel argues that data catalogs provide visibility but fail to deliver business value, comparing them to "buying JIRA and expecting agile practices." He advocates for shifting from restrictive data governance to data enablement through policy-based access control that considers user attributes, data sensitivity, and business context. Kasriel explains how AI-driven policy engines can learn from organizational behavior to automatically grant appropriate data access while maintaining compliance, ultimately reducing time-to-insight and unlocking missed business opportunities.

Patrick Thompson, co-founder of Clarify and former co-founder of Iteratively (acquired by Amplitude), joined Yuliia and Dumky to discuss the evolution from data quality to decision quality. Patrick shares his experience building data contracts solutions at Atlassian and later developing analytics tracking tools. Patrick challenges the assumption that AI will eliminate the need for structured data. He argues that while LLMs excel at understanding unstructured data, businesses still need deterministic systems for automation and decision-making. Patrick shares insights on why enforcing data quality at the source remains critical, even in an AI-first world, and explains his shift from analytics to CRM while maintaining focus on customer data unification and business impact over technical perfectionism. Tune in!

Kir Titievsky, Product Manager at Google Cloud with extensive experience in streaming and storage infrastructure, joined Yuliia and Dumky to talk about streaming. Drawing from his work with Apache Kafka, Cloud PubSub, Dataflow and Cloud Storage since 2015, Kir explains the fundamental differences between streaming and micro-batch processing. He challenges common misconceptions about streaming costs, explaining how streaming can be significantly less expensive than batch processing for many use cases. Kir shares insights on the "service bus architecture" revival, discussing how modern distributed messaging systems have solved historic bottlenecks while creating new opportunities for business and performance needs. Kir's medium - https://medium.com/@kir-gcp Kir's Linkedin page - https://www.linkedin.com/in/kir-titievsky-%F0%9F%87%BA%F0%9F%87%A6-7775052/

Serhii Sokolenko, founder at Tower Dev [https://tower.dev/] and former product manager at tech giants like Google Cloud, Snowflake, and Databricks, joined Yuliia to discuss his journey building a next-generation compute platform. Tower Dev aims to simplify data processing for data engineers who work with Python. Serhii explains how Tower addresses three key market trends: the integration of data engineering with AI through Python, the movement away from complex distributed processing frameworks, and users' desire for flexibility across different data platforms. He explains how Tower makes Python data applications more accessible by eliminating the need to learn complex frameworks while automatically scaling infrastructure. Sergei also shares his perspective on the future of data engineering, noting in which ways AI will transform the profession. Tower Dev - https://tower.dev/ Serhii's Linkedin - https://www.linkedin.com/in/ssokolenko/

Andrii Yasinetsky, CTO and co-founder of Diadia Health and AI expert, joined Yuliia to share his perspective on the current AI landscape and its future implications. Currently Andrii and his team are building an AI-first healthcare platform focused on metabolic and hormonal health. Andrii talks about how AI is changing both technology stacks and business economics. We discuss what's wrong with AI claims in enterprises and why it doesn't match reality, he points out the decreasing cost of intelligence, and why the middle layer of tech jobs may disappear within five years. Andrii also shares his take on how the recent US administration change has created a "timeline split" that could dramatically accelerate AI innovation, potentially transforming the global economy. Diadia Health - https://diadiahealth.com [https://diadiahealth.com/] Andrii's Linkedin - https://www.linkedin.com/in/yasinetsky/

Jess Kyle, a Data Engineering Leader with 13 years of experience across startups and enterprises, joined Yuliia to share her mission of making data work more enjoyable for everyone involved. As a leader of a data engineering team at a sports betting company, Jess shared how transparent communication can transform team dynamics and stakeholder relationships. In her work she challenges the common notion of "soft skills" in data, emphasizing that communication is a hard skill that should be screened for in interviews. Jess provides practical insights on managing overwhelm, delivering difficult feedback, and emphasizes why empathy and humility are the two most crucial qualities for data leaders - even though they're often undervalued in the tech industry. Jess's linkedin page - linkedin.com/in/jesskyle [https://www.linkedin.com/in/jesskyle]

Deepti Srivastava, Founder of Snow Leopard AI and former Spanner Product Lead at Google Cloud, joined Yuliia to chat what's wrong with current approaches to AI integration. Deepti introduces a paradigm shift away from ETL pipelines towards federated, real-time data access for AI applications. She explains how Snow Leopard's intelligent data retrieval platform enables enterprises to connect AI systems directly to operational data sources without compromising security or freshness. Through practical examples Deepti explains why conventional RAG approaches with vector stores are not good enough for business-critical AI applications, and how a systems thinking approach to AI infrastructure can unlock greater value while reducing unnecessary data movement. Deepti's linkedin [https://www.linkedin.com/in/thedeepti/]- https://www.linkedin.com/in/thedeepti/ Snowleopard.ai [http://snowleopard.ai/] - http://snowleopard.ai/

Bogdan Banu, Data Engineering Manager at Veed.io, joined Yuliia to share his journey of building a data platform from scratch at a fast-growing startup. As Veed's first data hire, Bogdan discusses how he established a modern data stack while maintaining strong governance principles and cost consciousness. Bogdan covered insights on implementing consent-based video data processing for AI initiatives, approaches to data democratization, and how his data team balancs velocity with security. Bogdan shared his perspectives on making strategic vendor choices, measuring business value, and fostering a culture of intelligent experimentation in startup environments. Bogdan's Linkedin - https://www.linkedin.com/in/bogdan-banu-a68a237/

Jason Touleyrou, Data Engineering Manager at Corewell Health joined Yuliia to discuss why most organizations struggle with data governance. He argues that data teams should focus on building trust through flexible systems rather than rigid controls. Challenging traditional data quality approaches, Jason suggests starting with basic freshness checks and evolving governance gradually. Drawing from his experience across healthcare and marketing analytics, he shares practical strategies for implementing governance during migrations and measuring data team value beyond conventional metrics. Jason's linkedin page - https://www.linkedin.com/in/jasontouleyrou/

Richard He, Founder at Fundamenta – Data Consultancy, former Engineering Director at Virgin Media O2  and creator of Practical GCP YouTube channel, joined Yuliia to discuss the critical concept of "cost of inaction" in data infrastructure modernization. Based on his two decades of experience, including several successful migrations, Richard emphasized the importance of proactive platform evolution over reactive large-scale migrations. He shared valuable insights on measuring ROI for platform teams, and bridging the gap between technical execution and business strategy. Richard's Linkedin - https://www.linkedin.com/in/shenghuahe/

Paolo Platter, CTO and co-founder of Agile Lab and Witboost, joined Yuliia to share how his 10 years of building custom data solutions for clients led to creating Witboost - a platform that helps big companies manage their data products at scale. One of their customers used Witboost to build over 250 data products in just 18 months, showing how well the platform works at scale. Paolo explained why setting rules for data teams becomes harder as companies grow, and shared how he shifted from saying "yes" to every client request as a consultant to building a product that works for many companies. Paolo Platter - https://www.linkedin.com/in/paoloplatter/

Amy Raygada, data consultant and founder of Cosmodata, recently joined the Thoughtworks team. Together with Yuliia, she unpacks what it's like to work as a data consultant in today's world, where AI challenges every organization. Amy shared her journey from individual contributor to consultant, offering insights on navigating organizational silos, the similarities between being an individual contributor and a consultant, and how to advise big organizations while making a meaningful impact. Amy Raygada linkedin - https://www.linkedin.com/in/amy-raygada/

Danilo Sato, AI Platform & Engineering Portfolio Leader at ThoughtWorks (16+ years), joined Yuliia and Scott to discuss enterprise AI readiness assessment and how implementation is progressing. Danilo shared a fascinating case study about how technical solutions often face non-technical barriers. He shared his views on data mesh principles intersecting with AI solutions and treating AI models as specialized data products. Danilo's Linkedin Page: linkedin.com/in/danilosato [https://www.linkedin.com/in/danilosato]

Martin Fiser, Field CTO at Keboola with 8 years at Google, joined Yuliia and Scott to challenge modern data stack complexity and costs. He advocates for all-in-one platforms over fragmented solutions, highlighting how companies waste up to 40% of time on tool integration. Martin shared insights on US-European cultural differences in data approaches and warned against "development by resume" culture where engineers prioritize trendy tools over business outcomes.

Scott Hirleman, founder of Data Mesh Radio, has a very diverse career, including a role as FinOps Manager. In this podcast, we sit down together to discuss his FinOps philosophy: cost containment, cost attribution, and cost forecasting. It's an insightful conversation on how to balance costs for data teams with both data and business considerations in mind.

Daniil Shvets, CEO and co-founder of ASAO DS: Data & AI Consulting Boutique, previously led various companies' Data Science and Product teams. Daniil sat down with Yuliia and Scott to share his opinion on Data Science being a business department with appropriate data skills rather than an IT department. He explained why having 54 ML models in one of the largest retailers in the USA is the wrong approach. Daniil also shared his views on the biggest challenges in perceiving Data Science's role. We also touched on AI and the consultancy business while Scott made all possible relationship analogies. :) Daniil's Linkedin: https://www.linkedin.com/in/daniilshvets/