Episoder
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In this episode of The Data Playbook, we explore what it really takes to build high-performance data teams.
Host Kris Peeters is joined by Rushil Daya, Senior Data Engineer at Dataminded, who shares practical lessons from years of leading successful data teams across industries.
They discuss:
The link between data success and business valueHow mentoring beats documentation when upskilling teamsWhy testing and CI/CD matter more than flashy toolsWhat makes stakeholder communication essentialWhy Agile is nothing without real feedback loopsđ Watch on YouTube: https://youtu.be/JEPPVakHfhA
đ More at www.dataminded.com
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In this episode of the Data Playbook podcast, we explore what it really takes to build sustainable, data-centric organizations, moving beyond tooling and dashboards toward lasting value.
Host Kris Peeters is joined by Jonny Daenen (Knowledge Lead at Dataminded), who shares insights from years of helping organizations evolve their data strategy across sectors. Together, they discuss why data platforms, domain-owned data products, and people-first operating models are the foundations of modern data success.
đ Topics covered:
Why dashboards arenât enough for true data maturityFrom central chaos to federated teams and self-serviceThe role of governance in enabling deliveryHow AI and LLMs reshape data tooling, ownership & valueWhat âdata-centricâ really means , and why most fail to get theređ Hosted by Kris Peeters
đ„ With Jonny Daenen, Dataminded
đ Visit our website for more.
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In this episode of The Data Playbook, we take a technical look at SQLMesh, a data transformation framework designed to improve the workflow and reliability of SQL-based data pipelines. Hosted by Kris Peeters, the episode features Michiel De Muynck, Senior Data Engineer at Dataminded, who provides a deep dive into SQLMeshâs internal mechanics, including its use of semantic analysis and isolated runtime environments.
Michiel outlines how SQLMesh differentiates itself from tools like dbt by incorporating a semantic parser for SQL, enabling structural validation and more precise error diagnostics during pipeline development. He also explains the implementation of virtual data environments, which allow data engineers to stage, test, and version transformations without impacting production datasets, supporting safer iteration and deployment processes.
đ§ Listen to more episodes on Spotify: Data Playbook Podcast
đ Visit our website for more: Website Link
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In this special episode of "The Data Playbook" podcast, recorded live at the Data Mesh Live Event in Antwerp, Kris Peeters speaks with Data Mesh pioneers Jacek Majchrzak and Andrew Jones. They explore how Data Mesh addresses critical challenges in data management, including data bottlenecks, governance, and decentralization. With years of experience in the field, both Jacek and Andrew share practical lessons from their journeys and offer actionable insights into implementing Data Mesh effectively.
The conversation covers:
Solving data bottlenecks through decentralized architecturesImproving governance with federated modelsAligning data strategy with business goals for impactful resultsUnderstanding the importance of incremental implementationMoving beyond "data silos" towards a more flexible, scalable approachJacek and Andrew provide real-world examples of how Data Mesh can transform your data infrastructure, sharing lessons on what works, what doesnât, and how to manage a successful Data Mesh implementation. If you're looking to overcome common data management challenges like governance and scalability, this episode is packed with practical advice.
Books Referenced by Our Speakers:
đ Data Mesh in Action by Jacek Majchrzak - https://a.co/d/4i5HUcY
đ Driving Data Quality with Data Contracts by Andrew Jones - https://amzn.eu/d/aMQRFH1
Stay tuned for more episodes on Data Mesh and other important topics in data architecture by following "The Data Playbook" on Spotify.
đ§ Watch the full episode on Youtubeâ
â đ Learn more on our website
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Join us in this episode of The Data Playbook as we explore the sense and nonsense of data modeling with Jonas De Keuster, VP of Product at VaultSpeed. Jonas takes us through his journey in the world of data automation, discussing the role of data integration, data vaulting, and how modern data products are built using structured models. From dimensional modeling to the complexities of integrating data across multiple systems, Jonas shares practical insights into how organizations can scale their data operations.
Topics covered include:
Data Modeling Techniques: Data Vault vs. Dimensional ModelingData Automation and IntegrationBuilding Data Products with Scalable ModelsHow to Manage Data Changes and Evolving Business NeedsReal-world Challenges in Data PlatformsWhether you're leading a data team or just beginning your journey, this episode is a must-listen for anyone interested in the future of data architecture. Tune in for expert advice on building integrated data solutions that deliver real business value.
To learn more, visit our website, or Watch more episodes on YouTube.
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What do you do when GDPR forces your cloud project to stopâand years later, you need to go back? In this episode, Niels Melotte, Data Engineer at Dataminded, unpacks the journey of a government agency that migrated from the cloud to on-prem and then back to the cloud again.
And hereâs the kicker: the Big Bang migration only took 14 hours. No downtime. No data loss. No angry users.
đ In this episode, we discuss:
Schrems II and why it sent European governments off the cloud
AWS Nitro Enclaves & external key management for GDPR compliance
Why the on-prem platform failed to meet uptime guarantees
What âpurpose-based access controlâ means and why it matters
The value of standardizing with dbt and Starburst
How data product thinking shaped the migration strategy
Lessons learned about trust, stakeholder communication, and platform maturity
This isnât a fluffy case study. Itâs a practical guide full of engineering tradeoffs, real-world headaches, and long-term lessons. A must-listen for data leaders, engineers, architects, and anyone dealing with sensitive data and complex infrastructure decisions.
đ§ Want more episodes?Watch or Listen to all episodes of The Data Playbook on Spotify: đ https://open.spotify.com/show/78z3kdyBSKiURz1VnTVP9l?si=781abec722264306
Show notes, episodes & resources:đ https://www.dataminded.com/resources/podcast
#CloudMigration #PublicSector #GDPR #DataGovernance #AWS #DataPlatform #dbt #Starburst #BigBangMigration #TheDataPlaybook #Dataminded
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In this episode of The Data Playbook, we dive deep into a critical, often-overlooked question: What does it mean to build sustainable data products? And no, weâre not just talking ESG dashboards or carbon reporting.
đïž Host Kris Peeters is joined by Geert Verstraeten, a seasoned data scientist, founder of Python Predictions, and now a Co-Lead at The Data Forestâa consultancy that puts purpose and sustainability at the core of every data project.
Over a candid and rich conversation, Geert shares:
How he transitioned from startup founder to corporate leader and back to startup lifeâwith a sustainability mission.Why many data projects fail not because of tech, but because of missed alignment and poor adoption.What sustainable data products really are: tools that not only minimize environmental impact, but also stand the test of timeâwell-documented, actually used, and aligned with real business needs.Why selecting the right clients and projects is the first step toward impact, not just profit.How the Data Forest scores potential engagements using a unique framework: Head, Heart, and Hands.đĄ Along the way, youâll hear thought-provoking takes on:
The role of documentation in sustainability.Why good data work isnât about building everything real-time or at scaleâespecially when youâre not Google.The paradox of GenAI and compute-heavy models in a world striving for tech responsibility.Whether you're a data engineer, architect, scientist, or team lead, this episode challenges you to rethink what a "good" data project looks like.
đ If youâve ever built something technically brilliant that no one used, this episode is for you.
Hit play to hear:
What drives Geert and his team at The Data ForestHow to make better decisions on project scoping and client fitWhy data professionals need to talk to stakeholders much earlierâand more often -
What Not to Build with AI: Avoiding the New Technical Debt in Data-Driven Organizations
In this episode of The Data Playbook, we explore a crucial and often overlooked question in the age of generative AI: not what to buildâbut what not to build.
Host Kris Peeters (CEO of Dataminded) is joined by seasoned data leaders Pascal Brokmeier (Head of Engineering at Every Cure) and Tim Schröder (AI & Data Transformation Lead in Biopharma), to talk about the dark side of unlimited AI capabilities: technical debt, fragmented systems, and innovation chaos.
Topics we dive into:
Why generative AI lowers the barrier to buildingâbut increases long-term complexity
The risks of treating LLMs as âmagical oraclesâ without governance
How RAG systems became the default architectureâand why thatâs dangerous
The zoo vs. factory dilemma: how to balance AI experimentation with structure
Master data vs. knowledge graphs vs. embeddings â when and why each breaks down
What Klarna got right (and wrong) by replacing SaaS tools with AI-generated internal apps
The growing importance of AI literacy, data maps, and platform thinking
Real-world examples of AI agents autonomously debugging systemsâand when thatâs terrifying
We ask tough questions like:
Are enterprises just building themselves into a new kind of mess, faster than ever before?
Is the AI hype driving us toward âbuild now, regret laterâ?
Should you really let every department build their own AI stack?
Whether you're a data engineer, data architect, AI product lead, or a data strategist, this episode is a must-listen. Weâre cutting through the hype to figure out where the real value isâand where the future tech debt is quietly piling up.
đ§ Key quote:"If you can't tell me why you're building it, maybe you shouldn't be building it at all."
đĄ Tune in to learn how to stay smart, intentional, and strategic when it comes to building with AI.
#TheDataPlaybook #DataEngineering #AIinBusiness #TechnicalDebt #RAG #LLMs #DataStrategy #EnterpriseAI #DataGovernance #DataLeadership #KnowledgeGraphs #GenerativeAI #AIinHealthcare #AIProduct #Dataminded
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What does it really take to build a modern data architecture from the ground up?
In our very first episode of The Data Playbook, host Kris Peeters, founder and CEO of Dataminded, sits down with Thorsten Foltz, seasoned data architect and engineer, to unpack what works (and what doesnât) when designing scalable, future-proof data platforms.
With a focus on real-world tradeoffs, this episode explores:
Cloud vs on-prem vs hybrid: how to choose the right infrastructureThe rise of Data Mesh and when it actually makes senseWhy fake news isnât just a media problemâitâs a data problem inside companies tooVendor lock-in, cloud sovereignty, and the growing relevance of European alternativesThe balance between open source and managed services: cost, control, and complexityWhy team culture and communication often make or break your data strategyWhat engineers can really expect from LLMs in the data stack (spoiler: they're not replacing data modeling any time soon)Whether you're a data engineer, architect, analyst, or tech leader, this conversation goes far beyond buzzwords. Youâll hear practical lessons, hard-earned insights, and a few uncomfortable truths about how companies actually manage data todayâand how they should rethink it for tomorrow.
