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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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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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 Just a mess and tangle of code and outdated processes, with no real way to understand how to get code from dev to production … without taking down the system.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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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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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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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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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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I’ve been a dog licking my wounds for some time now. Over on my Substack newsletter, I’ve been doing a small series on DSA (Data Structures and Algorithms). I tackled some of the easier stuff first, like Linked Lists, Binary Search, and the like. What’s more, I actually did most of it in Rust, since I’ve possibly, maybe slightly, every so slightly, fallen in love with Rust.

Like most relationships, it vacillates between pure adoration and utter hatred, depending on the problem at hand. When I did a recent article on Graphs, Queues, and BSF, I attempted it in Rust, and was struck a mighty blow, that borrow checker had me down. It seemed doable, but at the time, under time pressure to get the Newsletter out, I reverted to Python and moved on.

Alas, I’m back again, a glutton for punishment. This time I thought I should try another crack at parsing a graph with Rust, but in a real-life situation, no more made-up stuff.  Actual data, actual graph, here we go. All code is on GitHub.

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