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AWS Lambdas – Python vs Rust. Performance and Cost Savings.

Save money, save money!! Hear Hear! Someone on Linkedin recently brought up the point that companies could save gobs of money by swapping out AWS Python lambdas for Rust ones. While it raised the ire of many a Python Data Engineer, I thought it sounded like a great idea. At least it’s an excuse to […]

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Data Types in Delta Lake + Spark. Join and Storage Performance.

Hmm … data types. We all know they are important, but we don’t take them very seriously. I mean we know the difference between boolean, string, and integers, those are easy to get right. But we all get sloppy, sometimes we got the string and varchar route because we don’t spend enough time on the […]

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Ownership and Borrowing in Rust – Data Engineering Gold Mine.

As I started to use Rust on and off, more out of curiosity than anything, I discovered some specs of gold buried down in the depths. Some of the things I’m going to talk about, well … all of it, is probably fairly obvious to most Rust folk, but it’s enjoyable to learn what new […]

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Replacing Pandas with Polars. A Practical Guide.

I remember those days, oh so long ago, it seems like another lifetime. I haven’t used Pandas in many a year, decades, or whatever. We’ve all been there, done that. Pandas I mean. I would dare say it’s a rite of passage for most data folk. For those using Python, it’s probably one of the […]

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Using Rust to write a Data Pipeline. Thoughts. Musings.

  Rust has been on my mind a lot lately, probably because of Data Engineering boredom, watching Spark clusters chug along like some medieval farm worker endlessly trudging through the muck and mire of life. Maybe Rust has breathed some life back into my stagnant soul, reminding me there is a big world out there, […]

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Simplify Delta Lake Complexity with mack.

Anyone who’s been roaming around the forest of Data Engineering has probably run into many of the newish tools that have been growing rapidly around the concepts of Data Warehouses, Data Lakes, and Lake Houses … the merging of the old relational database functionality with TB and PB level cloud-based file storage systems. Tools like […]

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I asked ChatGPT to write a blog post about Data Engineering. Here it is.

Data engineering is a vital field within the realm of data science that focuses on the practical aspects of collecting, storing, and processing large amounts of data. It involves designing and building the infrastructure to store and process data, as well as developing the tools and systems to extract valuable insights and knowledge from that […]

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What is Apache Arrow? Asking for a friend.

We’ve all been in that spot, especially in tech. You wanted to fit in, be cool, and look smart, so you didn’t ask any questions. And now it’s too late. You’re stuck. Now you simply can’t ask … you’re too afraid. I get it. Apache Arrow is probably one of those things. It keeps popping […]

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Dataframe Showdown – Polars vs Spark vs Pandas vs DataFusion. Guess who wins?

There once was a day when no one used DataFrames that much. Back before Spark had really gone mainstream, Data Scientists were still plinking around with Pandas a lot. My My, what would your mother say? How things have changed. Now everyone wants a piece of the DataFrame pie. I mean it tastes so good, […]

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Why Data Migrations Suck.

I’ve often wondered what purgatory would be like, doing penance for millennia into eternity. It would probably be doing data migrations. I suppose they are not all that dissimilar from normal software migrations, but there are a few things that make data migrations a little more horrible and soul-sucking. Data migrations are able to slow […]

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