I’m a big fan of text expanders, espanso being my favorite one. As Xorg is no longer maintained, I recently switched to Wayland and the Gnome desktop environment. Unfortunately, I found out that Wayland support is experimental. This means espanso has to be installed from source. I browsed some forums and saw that installing espanso on Wayland is tricky. After some tinkering, I managed to get espanso running on my system: Void Linux x86_64 6.12.30_1 Gnome 47.4 on Wayland Install rustup Install rust via rustup. ...

I’m excited to share the latest episode of the Industrial Data Podcast featuring Lonnie Bowling, founder of Diemus Consulting. We explore the fascinating evolution of OT data integration, from basic SCADA trend-viewing to today’s cloud-based AI analytics. Lonnie shares how historians like OSIsoft PI transformed from simple tools to enterprise-wide platforms, while discussing the ongoing industry consolidation (AVEVA acquisition of OSIsoft) reshaping the landscape. Despite technological advances, we’re wrestling with old problems: the challenges of proprietary formats, inconsistent naming conventions, and questionable data quality persist as companies migrate to platforms like Databricks for advanced analytics. ...

Data good enough for operations is not necessarily analytics-ready. Exactly one month ago I published the first episode of my Watts in Your Data Podcast. In my opinion, a topic of critical importance that is all too often overlooked, especially with all the buzz around AI. In the most recent episode, I had the pleasure of inviting guest speaker Thomas Dhollander, co-founder of Timeseer.AI. Together we explored critical challenges in industrial time series data reliability and observability. ...

In this video I explain how to read data from an Airtable into a PySpark dataframe on Databricks. Airtable is a popular spreadsheet tool used at many enterprises. It offers additional features compared to other tools such as Microsoft Excel and Google Sheets

Today I published a 30 minute talk about my career in data thus far. I’ve made many mistakes along the way, but learned a tonne from them. Although I made many jumps over the years, I’m happy I always stuck around the central theme of data. Perhaps my talk can give you some inspiration if you feel stuck? Topics include: How I ended up in data after graduating in Materials Science and working in aluminium. How I started freelancing, but ended up employed again. Why I decided to freelance again What I did better the second time. Why I’m focusing on Databricks in the future. Listen to the podcast here: ...