This is an interesting one indeed, it’s one that teases and puzzles the brain to no end. Has the Data Warehouse finally died, has that unruly upstart the Lake House finally taken its place atop the seething mass of data we call home? Can we say that after all these decades the Data Warehouse Toolkit and Kimball is finally gone the way of the dinosaurs? Maybe. Probably. I don’t know.
I’ve been hacking around with tools and programming since Perl was a thing. I’ve worked the gambit of Data Platforms from large organizations to tiny startups, and all those in between. I’ve worked on Data Platforms that dropped ungodly amounts of money on SAP products, and places where we would build our own massive data processing platforms on Kubernetes.
Each to their own I guess.
I recently wrote on my Substack (Data Engineering Central) about how I used the new OpenAI o1 model to do some basic Data Engineering tasks surrounding PostgreSQL. It did ok. I’ve also been using CoPilot and ChatGPT for over a year now to assist me with my daily code that I have to write for one reason or another.
It is a Brave New World out there these days. The new tools and features come out faster than your mom on Sunday morning getting you ready for church. The same goes for the context and advice being produced on a myriad of platforms, the ole’ Like and Subscribe, and all that bit. It does make you wonder after a while, what you can trust, who has your best interest in mind, and who is selling you a bottle of snake oil, doesn’t it?
Today we talk about Data Modeling. Specifically Data Modeling in the new world we all live in christened The Lakehouse by our benevolent Vender Overlords.
Did you know there are only 3 types of Data Engineers? It’s true. I hope you are the right one.
Over the many years I’ve been pounding my keyboard … Perl, PHP, Python, C#, Rust … whatever … I, like most programmers, built up a certain disdain for what is called Low Code / No Code solutions. In my rush to worship at the feet of the code we create, I failed, in the beginning, to recognize some important axioms …
Polars is the hot new Rust based Python Dataframe tool that is taking over the world and destryoing Pandas even as we speak. You want the quick and dirty introduction to Polars? Look no farther.
I still remember the good ole days when Apache Spark was fresh and hot, hardly anyone was using it, except a few poor AWS Glue and EMR users … Lord have mercy on their ragged souls. It’s funny how that GOAT of a tool went from being used by a few companies for extremely large datasets … to today’s world, with Databricks, where Pandas-sized data is crunched with Spark.
Recently, for some unknown reason, I was pursuing the new Stackoverflow … called Reddit, for Data Engineering … and I ran across an interesting question … more or less it was related to “what makes a good Software Engineer … in a Data Engineering context.”
One thing I find myself doing these days (I am unsure how I feel about this), is teaching others to solve problems … Data Engineering problems to be specific. It’s not a hard stretch for most to imagine that what a person does at Senior+ software-type levels is just write good code all day.
I assure you, this is not the case typically.