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Date and DateTime Manipulation in Polars

One thing all Data Engineers are doomed to do in purgatory will be to solve different date and datetime problems in an endless loop. I’m sure of it. I can’t imagine anything worse, so that must be it. Either way the constant need to manipulate dates and datetimes are just a way of life, something that never ends and never changes. Also, it appears Polars is here to stay from what I can tell. Not a fad like that Data Mesh. Since Polars is here to stay, (I’ve already got it running in production at my company, (don’t mind if I bow)), we should probably take a gander at how to manipulate date and datetime objects from both the Dataframe and (if I have time) SQL perspective. See if we can find anything to complain about. I like to complain.

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The Ultimate Data Engineering Chadstack. Running Rust inside Apache Airflow.

Is there anything more Chad than Apache Airflow … and Rust? I think not you whimp. What two things do I love most? At the moment Rust and Airflow are at least somewhere at the top of that list. I wring my hands sometimes, wishing that things and technologies somehow come together into some bubbling soup and witches concoction from the depths. Then I had a strange thought while laying in bed one night.

What would happen if I ran my Rust inside my Apache Airflow? What would happen? Would the sun go dark? Would SQL Servers everywhere puke up their log files and go to Davey Jones’s locker? Birds fall from the sky? Why hasn’t anyone done this before, why isn’t anyone making this happen in real life?

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Introduction to using Rust Libraries (cargo and crates)

So perhaps you’re thinking it’s time to use Rust on your next project. You’ll find plenty of primers on how to get your feet wet in the language (and if you somehow made it this far without that much, The Book is that starting point), but maybe you’re feeling a bit lost amidst the seas of opportunity. While still growing, the Rust ecosystem has many great existing options to pull from, and you’re now asking “How do I even?”

If you’re wondering how to make sense of your third-party library ecosystem, I hope to get you thinking like a Rustacean.

If you’re stuck on trying to find the right library for you, jump down to the section on crates for a primer on discoverability. Otherwise, if you already know what you’re working with and are looking to understand it, keep reading to see how I do it.

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DuckDB + Delta Lake (the new lake house?)

I always leave it to my dear readers and followers to give me pokes in the right direction. Nothing like the teaming masses to set you straight. Recently I was working on my Substack Newsletter, on the topic of Polars + Delta Lake, reading remove files from s3 … I left a question open on my LinkedIn account.

I had someone jog my leaky memory in favor of DuckDB. I haven’t touched DuckDB in some time, and I’m sure it’s under heavy development what with that Mother Duck and all.

So, it’s time to talk about DuckDB + Delta Lake.

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The Role of DevOps and CI/CD in Data Engineering

In the vast world of data, it’s not just about gathering and analyzing information anymore; it’s also about ensuring that data pipelines, processes, and platforms run seamlessly and efficiently. Nothing screams “why are flying by night,” than coming into a Data Team only to find no tests, no docs, no deployments, no Docker, no nothing. Just a mess and tangle of code and outdated processes, with no real way to understand how to get code from dev to production … without taking down the system.

This is where the principles of DevOps and Continuous Integration/Continuous Deployment (CI/CD) come into play, especially in the realm of data engineering. Let’s dive into the importance of these practices and how they’ve become indispensable in modern data engineering workflows.

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The Case of the Mysterious Recursive CTE

I still remember that day. A day that shall live on in infamy in my mind. Well over a decade ago, in the days when SQL Server roamed the land devouring souls on the Altar of Stored Procedures. There was only one tool available at the time. SQL. That’s it. There was one problem that had to be solved.

The answer? A recursive CTE.

At the same time … both a demon of the dark and a shining angel from the heavens. Just depends on your view.

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Senior Engineer – The Number One Skill

Do you think I’m just trying to get you to click? Maybe. Maybe not. After working in and around Data Teams for well over a decade, with both the smartest people to touch the keyboard, and the others, it’s become quite clear to me what the number one skill that identifies a Senior level Engineering from the peons rummaging around in the StackOverflow garbage can for snippets, is.

I’m sure there will be hand-wringing, curses, tears, and generally weeping and moaning in the land, like some medieval plague that has swept away everything we hold dear. So just calm yourselves, sit down, and get your angry little fingers off that keyboard. Hear me out.

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Introduction to AWS Lambda (deployment)

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Introduction to Delta Lake

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Polars vs Pandas. Inside an AWS Lambda.

Nothing gives me greater joy than rocking the boat. I take pleasure in finding what people love most in tech and trying to poke holes in it. Everything is sacred. Nothing is sacred. I also enjoy doing simple things, things that have a “real-life” feel to them. I suppose I could be like the others and simply write boring tutorials on how to do the same old thing for the millionth time.

Ugh. No thanks.

Today I want to do something spectacularly normal. Something Data Engineers do. I’m simply going to write an AWS Lambda to process some data, one with Polars, and one with Pandas. What do I hope to accomplish?

Well, I can usually make a few people mad. AWS Architectures and fan clubs, Polars people, Pandas people, and the general public at large. Bring it.

All code on GitHub.

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