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

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.

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

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

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.comLearn Data Engineering with me: learndataengineering.com

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!

In this Hero Talk episode, I had the pleasure of chatting with Ben Rogojan, better known as the "Seattle Data Guy." Ben is a data engineer, YouTuber, and freelancer with a background at Facebook. He's become a go-to expert on freelancing for engineers, particularly in the data space.We dive into Ben's journey from being a full-time engineer to making the switch to freelancing, how he built his own business, and the unique challenges freelancers face in this space.We also explore how to break into freelancing, the value of specializing in a specific skill, and practical tips on landing your first freelance clients.

In this episode of the Plumbers of Data Science podcast, I'm sharing some exciting updates about the future of Learn Data Engineering and a big new service we’re launching—recruiting!I explain how this new offering will help engineers find their next career move while connecting companies with top talent. Tune in to hear more about how the Academy, Coaching, and now recruiting fit together into one ecosystem designed to support your career growth. Let me know your thoughts in the comments—are you excited about this new direction?

In this episode of the Plumbers of Data Science podcast, I’m sharing my thoughts on why data modeling isn’t as complicated as people make it out to be. You hear about courses and tutorials that stretch for hours—but is it really that hard?I’ll break down the two main things you need to focus on when modeling data and explain why, once you’ve got those down, the rest falls into place.

In this Hero Talk episode, I talk with Mezue, a seasoned Data Engineer with expertise in Azure Databricks Data Engineering. We cover his journey from Electrical Engineering to Data Engineering and discuss the key skills, like Python, SQL, and Spark, that are essential in the field.Mezue also shares his experience running an Azure Databricks bootcamp and offers advice on how to break into Data Engineering, especially in Cloud environments. We also touch on the challenges of finding junior roles and how to stand out by working on practical projects.

In this Hero Talk episode, I chat with Susan Walsh, the “Classification Guru,” known for her expertise in cleaning and classifying messy data.We dive into her unexpected journey into the data world, starting with a spend analytics job, and how that led to her founding her own business focused on dirty data. Susan shares the unique challenges businesses face with poor data quality, explaining why 99.9% of data problems are actually people problems.We also explore practical ways to deal with these issues, such as finding those "crappy" data cleaning jobs to gain experience, and the importance of consistent data maintenance to prevent future headaches. From addressing dirty CRM systems to battling fraud, Susan’s stories highlight how critical clean data is for business success.