Summary

Andreas Kretz is known for in-depth explanations of data engineering concepts and practical advice, particularly on cloud infrastructure and big data technologies.

OnAir Post: Andreas Kretz

News

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 Engineering

➡️ Why switching from employee to full-time author wasn’t planned, but necessary

➡️ How he uses a “Second Brain” system to manage and publish his knowledge

➡️ Why writing is a tool for learning — not just sharing

The concept of convergent evolution in data tooling: when old and new solve the same problem

The 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

Overview

Hi my name is Andreas Kretz. I teach Data Engineering at LearnDataEngineering.com. My Academy at learn data engineering will tech you everything to become a Data Engineer or use Data Engineering in your current job. On this channel I talk about tools, techniques and topics that I experience in my day to day work. As an Engineer you figure out how to ingest, process and store data to enable Data Scientists, Analysts or customers to do awesome stuff. Using tools like AWS, GCP, Apache Spark Apache Kafka, MongoDB, Python, Databricks, Apache Airflow and many more. That’s what data engineering, the plumbing of data science is all about.

Source: YouTube channel

Web Links

Videos

Most important Python skills for Data Engineers!

(05:45)
By: Andreas Kretz

Too many people get scared about these big Python requirements. Then they get a course that sends them down the rabbit hole. Relax! There are only a few packages that you should know as a Data Engineer. Here’s what you need.