Plumbers of Data Science

Andreas Kretz
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Data Engineering is the plumbing of data science. Almost invisible, but super important and a big mess when done wrong. We talk about interesting Data Engineering trends and topics. I also train Data Engineering in my Data Engineering Academy at LearnDataEngineering.com

  1. 8月6日

    #126 The Cloud, Java, and the Future of Serverless – with Vadym Kazulkin

    In this podcast episode, I’m talking with Vadym Kazulkin, AWS Serverless Hero and Principal Cloud Architect. He’s part of the AWS community for years now, speaking at events, testing new features early, and helping the community grow. We talked about: How serverless is being used in the real worldWhy Java still has a place in modern cloud setupsWhat stops big companies from going all inHow AWS is evolving — and what’s missingAnd what engineers should know before going “cloud-native”  If you're building in the cloud or just trying to make smart decisions in a fast-moving space, this episode has a lot for you. Connect with Vadym via LinkedIn: https://www.linkedin.com/in/vadymkazulkin/

    47 分鐘

  2. 7月22日

    #125 AI Hype vs. Reality – with Christina Stathopoulos

    In this episode, I’m joined by Christina Stathopoulos, a former Googler who now works independently as a data & AI evangelist, trainer, and advisor. Not only did we talk about her path from data engineering into data science, and eventually into teaching and strategic consulting. We also covered: Why many companies still struggle with data & AI basicsThe hype vs. reality of generative AI (and how to cut through the noise)Why soft skills like communication, critical thinking, and storytelling are underrated in techHow regulation in Europe might be slowing things down, and why AI won’t wait for bordersThe difference between online and in-person learning (and why human connection still matters)and much more! All in all: A thoughtful, honest conversation full of insights on AI, data culture, and the human side of technology. Get in touch with Christina via LinkedIn: https://www.linkedin.com/in/christinastathopoulos/

    50 分鐘

  3. 7月9日

    #124 These Skills Get You a Data Consulting Job – with Tom Schamberger

    In this episode, I’m talking with Tom Schamberger from the German consultancy msg. He leads their cloud data platform team and has a super interesting background: started coding Java at 12, co-founded startups, and now helps big companies design scalable data platforms. We talk about: What it really takes to be successful in data engineering consultingWhy soft skills matter just as much as tech skillsHow consulting projects actually work — from Excel chaos to full platformsThe role of tools like Databricks, Snowflake, and Microsoft FabricAnd why being tool-agnostic might be your biggest advantage If you're curious about consulting, data platforms, or just want to hear what a data engineer's job looks like behind the scenes, dive right into it!

    44 分鐘

  4. 6月19日

    #123 Building Fast and Fun Data Projects - with Mehdi Ouazza

    In this episode, I sit down with Mehdi Ouazza - data tinkerer, indie hacker, and content creator - who's always up to something interesting in the world of data and AI. We started with DuckDB but quickly veered off into much more exciting territory: side projects, voice-to-SQL with actual quacks, the power of local models, and why WebGPU might be one of the most underrated browser technologies today. We also talked about how we teach and learn data engineering in 2025: the importance of fun, interactivity, and why we both dream of creating a data engineering game that’s part "Among Us" and part serious skills training. Mehdi shares what tools he's using, where he sees GenAI actually helping—not replacing—engineers, and how he's building courses and meetups that inspire creativity in technical work. Perfect for data folks who like to experiment, educators looking for inspiration, or anyone wondering how far a fun idea can go with the right mix of curiosity and tooling.

    1 小時 17 分鐘

  5. 6月11日

    #122 Why Writing Is Thinking , and What Data Engineers Can Learn from It - with Simon Späti

    In this podcast episode, I’m joined by Simon Späti, long-time BI and data engineering expert turned full-time technical writer and author of the living book Data Engineering Design Patterns.  We talk about: His 20-year journey from SQL-heavy BI to modern Data EngineeringWhy switching from employee to full-time author wasn’t planned, but necessaryHow he uses a “Second Brain” system to manage and publish his knowledgeWhy writing is a tool for learning, not just sharingThe concept of convergent evolution in data tooling: when old and new solve the same problemThe underrated power of data modeling and pattern recognition in a hype-driven industry  Simon also shares practical advice for building your own public knowledge base, and why Markdown and simplicity still win in the long run. Whether you're into tools, systems, or lifelong learning, this one’s a thoughtful deep dive. *** About Simon Späti: Simon is a Data Engineer and Technical Author with 20+ years of experience in the data field. He's the author of the Data Engineering Blog (ssp.sh), curator of the Data Engineering Vault (vault.ssp.sh), and currently writes a book about Data Engineering Design Patterns (dedp.online). Simon maintains an awareness of open-source data engineering technologies and enjoys sharing his knowledge with the community. Socials: Bluesky, LinkedIn, Twitter/X, YouTube

    1 小時 5 分鐘

  6. 5月19日

    #121 From Application Dev to AWS Hero: A Journey in Tech & Impact - with Johannes Koch

    In this episode, I’m joined by Johannes Koch, Principal Engineer and AWS DevTools Hero, to talk about the real DevOps mindset, the evolution of developer experience, and how community work changed his career. Johannes shares how starting in QA and support gave him a unique edge in understanding users, why building a proper CI/CD pipeline should come before writing code, and how the AWS Community Builders program helped him grow into his current role. We also dive into: What DevOps culture actually means beyond automationHis take on GitOps and why CI/CD is still underratedBehind the scenes of the AWS Heroes ProgramCreating content, mentoring others, and avoiding the trap of tech hypeWhether you're starting out in DevOps or deep into cloud architecture, Johannes' insights are packed with value for data and AI professionals. Links: Johannes on LinkedInYouTube: @cicdonawsAWS Community Day DACH

    1 小時 3 分鐘

  7. 5月8日

    #120 Teaching Data Engineering Like It’s Done on the Job - with Deepak Goyal

    In this episode, I sit down with Deepak Goyal, the founder of AzureLib, to talk all things data engineering, cloud platforms, and how to teach the next generation of engineers. We explore why Azure became his go-to, how real-world projects beat theory every time, and why tools like ChatGPT are great assistants, but no substitute for structured learning and solid fundamentals. Follow Deepak on LinkedIn: https://www.linkedin.com/in/deepak-goyal-93805a17/ Check out AzureLib: azurelib.com Learn Data Engineering with me: learndataengineering.com

    49 分鐘

  8. 2024/11/04

    #119 Recruiting is harder than I thought

    In this episode of the Plumbers of Data Science podcast, I dive into the challenges of recruiting today, from overwhelming job application volumes to reaching out directly to recruiters. I’m testing new strategies to make the process smoother for everyone involved, focusing on fresh job listings and fostering connections with hiring managers who need skilled engineers. My goal? To secure five job placements in Germany by year’s end! Have thoughts on today’s job market, or tried the Easy Apply feature yourself? Drop a comment below—I’d love to hear your experience!

    10 分鐘
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簡介

Data Engineering is the plumbing of data science. Almost invisible, but super important and a big mess when done wrong. We talk about interesting Data Engineering trends and topics. I also train Data Engineering in my Data Engineering Academy at LearnDataEngineering.com

資訊

  • 創作者
    Andreas Kretz
  • 活躍年代
    2017年 - 2025年
  • 集數
    125
  • 年齡分級
    兒少適宜
  • 版權
    © Andreas Kretz
  • 節目網站
    Plumbers of Data Science

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