De wereld van ondernemen verandert razendsnel. AI en slimme Agents zijn geen toekomst meer, maar praktijk. Wat betekent dit voor het MKB? In deze ronde tafel gaan 15 ondernemers in gesprek over hun ervaringen, kansen en uitdagingen met AI. Hoe staat jouw organisatie ervoor, welke hobbels kom je tegen en hoe ziet je bedrijf er over twee jaar uit? Deze sessie biedt inspiratie, kennisdeling en concrete handvatten om kansen te benutten en risico’s te beperken.

De Nederlandse industrie kampt al jaren met een toenemend personeelstekort. Veel technici en onderhoudsmonteurs bereiken binnen enkele jaren hun pensioenleeftijd, terwijl er onvoldoende instroom is van jongere technici om hen te vervangen. Sleuteltechnologieën voor digitalisering, zoals generatieve AI, large language models (LLMs) en digital twins, bieden een oplossing in de vorm van de virtuele servicemonteur. Dit systeem combineert real-time data met storingsanalyses en documentatie om monteurs te ondersteunen bij het op afstand diagnosticeren en oplossen van problemen.

Tijdens onze ronde tafel sessie gaan we aan de slag met hoe Rabobank zich beschermt tegen het verzamelen van slechte data afkomstig uit de website en de app. Door het toepassen van verschillende technieken leggen we de fundering voor solide data kwaliteit om ons klaar te stomen voor, onder andere, AI.

Nederland beschikt over een indrukwekkend ecosysteem van innovatieve AI-startups en scale-ups. Tijdens deze sessie leer je hoe de AI Coalitie voor Nederland (AIC4NL) deze bedrijven ondersteunt bij het realiseren van (internationale) doorbraken via het programma Breaking Barriers. Tevens maak je kennis met inspirerende pioniers op het gebied van dataconsolidatie, privacy vriendelijke data-analyse en digitale authenticiteitscontrole.

This session explores practical knowledge graph use cases that help organizations connect enterprise data at scale. Learn how intelligent graphs turn complexity into advantage, enabling real-time impact analysis, adaptive AI, and decentralized decisions. Discover real-world examples and the tech stack behind this data-driven transformation.

Unlock efficient AI! This playbook explores Small Language Models (SLMs) as a cost-effective alternative to large LLMs. Learn to select, deploy (local/cloud), and utilize these powerful, often open-source models for high-value, targeted tasks.

As enterprises accelerate AI adoption, the challenge lies not just in innovation but in governing AI effectively to maximize business value. This session explores how Agentic AI Governance provides a structured yet adaptive framework to drive measurable impact. Through real-world case studies, we'll uncover success stories and challenges organizations face in balancing compliance, trust, and efficiency. Attendees will gain actionable best practices for implementing AI governance at scale while preparing for the evolving regulatory and enterprise landscape.

Session en ligne présentant les différences entre Data Analytics et Data Science & AI, afin d’aider à choisir la formation adaptée. Le rôle clé de l’IA dans les formations et comment elle est intégrée, ainsi que des informations pratiques sur le programme, les formats, le financement et les admissions.

Summary In this episode of the Data Engineering Podcast Hannes Mühleisen and Mark Raasveldt, the creators of DuckDB, share their work on Duck Lake, a new entrant in the open lakehouse ecosystem. They discuss how Duck Lake, is focused on simplicity, flexibility, and offers a unified catalog and table format compared to other lakehouse formats like Iceberg and Delta. Hannes and Mark share insights into how Duck Lake revolutionizes data architecture by enabling local-first data processing, simplifying deployment of lakehouse solutions, and offering benefits such as encryption features, data inlining, and integration with existing ecosystems.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Hannes Mühleisen and Mark Raasveldt about DuckLake, the latest entrant into the open lakehouse ecosystemInterview IntroductionHow did you get involved in the area of data management?Can you describe what DuckLake is and the story behind it?What are the particular problems that DuckLake is solving for?How does this compare to the capabilities of MotherDuck?Iceberg and Delta already have a well established ecosystem, but so does DuckDB. Who are the primary personas that you are trying to focus on in these early days of DuckLake?One of the major factors driving the adoption of formats like Iceberg is cost efficiency for large volumes of data. That brings with it challenges of large batch processing of data. How does DuckLake account for these axes of scale?There is also a substantial investment in the ecosystem of technologies that support Iceberg. The most notable ecosystem challenge for DuckDB and DuckLake is in the query layer. How are you thinking about the evolution and growth of that capability beyond DuckDB (e.g. support in Trino/Spark/Flink)?What are your opinions on the viability of a future where DuckLake and Iceberg become a unified standard and implementation? (why can't Iceberg REST catalog implementations just use DuckLake under the hood?)Digging into the specifics of the specification and implementation, what are some of the capabilities that it offers above and beyond Iceberg?Is it now possible to enforce PK/FK constraints, indexing on underlying data?Given that DuckDB has a vector type, how do you think about the support for vector storage/indexing?How do the capabilities of DuckLake and the integration with DuckDB change the ways that data teams design their data architecture and access patterns?What are your thoughts on the impact of "data gravity" in today's data ecosystem, with engines like DuckDB, KuzuDB, LanceDB, etc. available for embedded and edge use cases?What are the most interesting, innovative, or unexpected ways that you have seen DuckLake used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DuckLake?When is DuckLake the wrong choice?What do you have planned for the future of DuckLake?Contact Info HannesWebsiteMarkWebsiteParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links DuckDBPodcast EpisodeDuckLakeDuckDB LabsMySQLCWIMonetDBIcebergIceberg REST CatalogDeltaHudiLanceDuckDB Iceberg ConnectorACID == Atomicity, Consistency, Isolation, DurabilityMotherDuckMotherDuck Managed DuckLakeTrinoSparkPrestoSpark DuckLake DemoDelta KernelArrowdltS3 TablesAttribute Based Access Control (ABAC)ParquetArrow FlightHadoopHDFSDuckLake RoadmapThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Worms without eyes shouldn’t see colour — yet C. elegans can. In this episode, we dive into a landmark study that shows how worms use blue-to-amber light ratios to make foraging decisions. When exposed to toxic blue pigments like pyocyanin, worms avoid them — but only under white light. The twist? They do it all without opsins.

We explore:

How worms detect and avoid blue-pigment-secreting P. aeruginosa Why light potentiates avoidance, but only for certain spectral ratios How lite-1 and GUR-3 receptors mediate spectral sensitivity Natural variation in colour preference across wild strains The discovery that stress-related genes jkk-1 and lec-3 underlie colour-guided behaviour

This episode uncovers a new form of opsin-free colour vision, expanding our understanding of how simple organisms read complex environments.

📖 Based on the research article: “C. elegans discriminates colors to guide foraging” Dipon Ghosh, Dongyeop Lee, Xin Jin, H. Robert Horvitz & Michael N. Nitabach Published in Science (2021) 🔗 https://doi.org/10.1126/science.abd3010

🎧 Subscribe to the WOrM Podcast for more full-organism surprises in perception, evolution, and behaviour.

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠⁠www.veerenchauhan.com⁠⁠ 📧 [email protected]

Today, we’re joined by Mats Persson, CEO of Umbraco, provider of the world’s leading open-source CMS platform. We talk about: The impacts of AI in developing and managing contentWhy content management systems will always surviveThe pluses and minuses of open source and AI helping write codeRisks of being too enthusiastic about your own opportunity & productThe challenges of GEO (Generative Engine Optimization), considering there's not one standard system

Discover how small AI prompts can transform your productivity. In this episode, Mukundan explains the 80‑20 rule. Mukundan demonstrates AI workflows for scheduling and focus, and introduces a Prompt Wallet to help you beat decision fatigue. Perfect for anyone seeking practical AI and better time management. Episode Highlights 80‑20 Rule Explained: Most of your progress comes from a few critical actions. Learn how to identify them with prompts.Prompt Structure 101: Context, constraints, desired outcome, and a tiny first step.Classic Prompts: 10‑minute workouts, zero‑based budgeting, Pomodoro study plans, teach‑back outlines, and Power Clean 15.New Productivity Hacks: Time blocking, task prioritization, interrupt handling, focus sprints, and daily review.Live Demo: Mukund feeds tasks into the AI and shows how it schedules a day, orders tasks by impact, manages interruptions, and sets up a focus sprint.Seven‑Day Challenge: Try one prompt per day and track whether you start within 60 seconds. Join the conversation with #PromptWalletChallenge.Takeaways & Actions Start with your context and constraints. Ask for a plan and a micro‑action.Most results come from a handful of well‑chosen tasks. Use prompts to find them.Commit to the seven‑day challenge. Sign up for the free newsletter and email Mukundan your progress.Download the free Prompt Wallet PDF to keep all the scripts handy.Support the show by subscribing and leaving a review; tell your friends if you found value.Links & Resources Prompt Wallet PDF: Get your free copyRecording Partner: Riverside → Sign up here (affiliate)Host Your Podcast: RSS.com (affiliate )Research Tools: Sider.ai (affiliate)Join the Newsletter: Free Email Newsletter to receive practical AI tools weekly.Join the Discussion (comments hub): https://mukundansankar.substack.com/notes🔗 Connect with Me:Website: Data & AI with MukundanTwitter/X: @sankarmukund475LinkedIn: Mukundan SankarYouTube: Subscribe

Before you start cramming tools to land a data job, ask yourself this: What tools are data analysts actually using every day? In this episode, I went straight to the pros—analysts at Google, Amazon, Apple, Tesla, Humana, Veterans United, 7-Eleven, and more—to hear which tools truly power their work. ✨ Try Julius today at https://landadatajob.com/Julius-YT 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 - Introduction 00:22 - Sundas Khalid (Google and Amazon) 03:10 - Jen Hawkins (Apple) 06:17 - Ryan Ponder (Veterans United) 07:32 - Alex Sanchez (7-Eleven) 09:37 - Jason Bryll (Healthcare Analytics Expert) 12:47 - Erin Shina (Humana) 14:54 - Lily BL (Tesla) 🔗 Watch my interviews and connect with our data experts! Sundas 🤝 LinkedIn: https://www.linkedin.com/in/sundaskhalid/ 🎥 YouTube: https://youtu.be/e53U55HbBog?si=_hQkB2EuuD1pFsg7 Jen 🤝 LinkedIn: https://www.linkedin.com/in/jeandriska/ 🎥 YouTube: https://youtu.be/f-BWp_IJZ-I?si=llWBc5hIW80SmeEd

Ryan 🤝 LinkedIn: https://www.linkedin.com/in/rtponder/ 🎥 YouTube: https://youtu.be/bH0wfE342R0?si=iN1ftUN31LbstdRw

Alex 🤝 LinkedIn: https://www.linkedin.com/in/ale-san/ 🎥 YouTube: https://youtu.be/VfrTaw27rDc?si=IlwL7FJLdUvlbbms

Jason 🤝 LinkedIn: https://www.linkedin.com/in/jason-bryll/ 🎥 YouTube: https://youtu.be/Qh4RBY5GwUY?si=HCvF80qw7gVbL0dc

Erin 🤝 LinkedIn: https://www.linkedin.com/in/erinshina/ 🎥 YouTube: https://youtu.be/5gSUqk1AiWM?si=MPF3oRY45B2DTQ2P

Lily 🤝 LinkedIn: https://www.linkedin.com/in/lilybl/ 🎥 YouTube: https://youtu.be/AB2McisjPTM?si=yw_2gCWtBcQDFGWf

🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Comment le programme est structuré, et les types de projets que vous développerez, des applications web classiques aux outils assistés par l’IA.

The manufacturing floor is undergoing a technological revolution with industrial AI at its center. From predictive maintenance to quality control, AI is transforming how products are designed, produced, and maintained. But implementing these technologies isn't just about installing sensors and software—it's about empowering your workforce to embrace new tools and processes. How do you overcome AI hesitancy among experienced workers? What skills should your team develop to make the most of these new capabilities? And with limited resources, how do you prioritize which AI applications will deliver the greatest impact for your specific manufacturing challenges? The answers might be simpler than you think. Barbara Humpton is President and CEO of Siemens Corporation, responsible for strategy and engagement in Siemens’ largest market. Under her leadership, Siemens USA operates across all 50 states and Puerto Rico with 45,000 employees and generated $21.1 billion in revenue in fiscal year 2024. She champions the role of technology in expanding what’s humanly possible and is a strong advocate for workforce development, mentorship, and building sustainable work-life integration. Previously, she was President and CEO of Siemens Government Technologies, leading delivery of Siemens’ products and services to U.S. federal agencies. Before joining Siemens in 2011, she held senior roles at Booz Allen Hamilton and Lockheed Martin, where she oversaw programs in national security, biometrics, border protection, and critical infrastructure, including the FBI’s Next Generation Identification and TSA’s Transportation Workers’ Identification Credential. Olympia Brikis is a seasoned technology and business leader with over a decade of experience in AI research. As the Technology and Engineering Director for Siemens' Industrial AI Research in the U.S., she leads AI strategy, technology roadmapping, and R&D for next-gen AI products. Olympia has a strong track record in developing Generative AI products that integrate industrial and digital ecosystems, driving real-world business impact. She is a recognized thought leader with numerous patents and peer-reviewed publications in AI for manufacturing, predictive analytics, and digital twins. Olympia actively engages with executives, policymakers, and AI practitioners on AI's role in enterprise strategy and workforce transformation. With a background in Computer Science from LMU Munich and an MBA from Wharton, she bridges AI research, product strategy, and enterprise adoption, mentoring the next generation of AI leaders. In the episode, Richie, Barbara, and Olympia explore the transformative power of AI in manufacturing, from predictive maintenance to digital twins, the role of industrial AI in enhancing productivity, the importance of empowering workers with new technology, real-world applications, overcoming AI hesitancy, and much more. Links Mentioned in the Show: Siemens Industrial AI SuiteConnect with Barbara and OlympiaCourse: Implementing AI Solutions in BusinessRelated Episode: Master Your Inner Game to Avoid Burnout with Klaus Kleinfeld, Former CEO at Alcoa and SiemensRewatch RADAR AI where...

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.

Tristan talks with Mikkel Dengsøe, co-founder at SYNQ, to break down what agentic coding looks like in analytics engineering. Mikkel walks through a hands-on project using Cursor, the dbt MCP server, Omni's AI assistant, and Snowflake. They cover where agents shine (staging, unit tests, lineage-aware checks), where they're risky (BI chat for non-experts), and how observability is shifting from dashboards to root-cause explanations. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.