How to be Better Than Everyone Else

Ok. Get off your high horse. You are human just like the rest of us. Just like your ancient ancestors who were throwing rocks and sticks at each other a thousand years ago … you are looking for a leg up on the competition. Isn’t that the world we live in? At the end of the day, no one is looking out for you, besides you.

Do you know what all those famous “influencers” have over you? Those you look at with boiling bitterness buried somewhere inside you. Besides a smattering of luck here and there, there is one other trait they have that most people don’t.

There are general branches and streams of actions that come off that main branch. But without that central drive, all else is useless.

What is it that apparently 1% of the data population has that the 99 don’t? Simply put, in the words of your grandparents, hard work.

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Fleetclusters for Databricks + AWS to reduce Costs.

Show me the money. That’s what it’s all about. I have a question for you, to tickle your ears and mind. Get you out of that humdrum funk you are in. Here is my question, riddle me this all you hobbits.

“Of what use is, and what good does the best and most advanced architecture provide if your bosses boss starts to look at the cost and starts to ask questions?”

The thing is, as engineers and developers we love to do fun stuff. We follow the crowds, don’t we? We are like those lemmings, we just do what is cool.

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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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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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Introduction to AWS Lambda (deployment)

Introduction to Delta Lake

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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Ballista (Rust) vs Apache Spark. A Tale of Woe.

Sometimes it seems like the Data Engineering landscape is starting to shoot off into infinity. With the rise of Rust, new tools like DuckDB, Polars, and whatever else, things do seem to shifting at a fundamental level. It seems like there is someone at the base of a titering rock with a crowbar, picking and prying away, determined to spill tools like Java, Scala, Python, Spark, and Airflow, the things we’ve known and loved for years, from their lofty thrones.

Maybe they all have had their time in the Data Engineering sun, maybe it’s time to shake things up. It seems to be happening. It’s always hard to have those we hold dear be poked and prodded at. I’ve been using Spark since before it was cool, so when I started to hear the word Ballista start to show up here and there, I took note.

Besides, I’ve been dabbling my grubby little fingers in Rust for some months now, and have seen The Light. Is it possible I could be living at the dawn of a new era? A new and exciting frontier of Data Engineering, finally, after all this time? Could Rust really take over? Will something like Ballista pull that old Spark from its distributed processing tower and claim its rightful place?

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