How I build what is essentially a self-service Data Lake at home to narrow down the search area for a new house, instead of using Zillow like a normal person, using Spark, Iceberg, and Python.
A quick project to write a Telegram chat bot that can control a camera, connected to a Raspberry Pi, to take pictures of things; or: Why I refuse to trust IoT vendors and do everything myself
Part 2 of throwing Raspberry Pis at the pepper plants in my garden: On the topics of 3D printing, more bad solder jobs, I2C, SPI, Python, go, SQL, and failures in CAD.
On how growing vegetables is more complicated than it looks, why bad soldering still works, on moving individual bits around, and what I learned about using technology where one probably does not need technology.
On the joy of inheriting a rather bad dataset - dissecting ~120GB of terrible Google Takeout data to make it usable, using Dataflow/Beam, go, Python, and SQL.
One question I do get in earnest quite frequently is why I put up with running GNU/Linux distributions for development work. An attempt at a simple response.
In Part 2 of our comparison of Python and go from a Data Engineering perspective, we'll finally take a look at Apache Beam and Google Dataflow and how the go SDK and the Python SDK differ, what drawbacks we're dealing with, how fast it is by running extensive benchmarks, and how feasible it is to make the switch
Exploring golang - can we ditch Python for go? And have we finally found a use case for go? Part 1 explores high-level differences between Python and go and gives specific examples on the two languages, aiming to answer the question based on Apache Beam and Google Dataflow as a real-world example.