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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 languages have to offer and ingest that knowledge into what we do, in this case, Data Engineering. There are some special things about Rust that can us all write better data pipelines and transformations.

Just like Scala brought immutability to legions of Data Engineers, Rust is going to bring Ownership and Borrowing through its memory model. Like some ancient King traveling lands throwing handfuls of coins to beleaguered subjects, groveling on the ground for scraps, such is Rust traveling the weary lands of Data Engineering.

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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, full of new and beautiful things to explore, just waiting for me.

I’ve written some Rust a little here and there, but I’ve been meditating on what it would look like to write an entire pipeline in Rust, one that would normally be written in Python. Would it be worthwhile? The cognitive overburden of solving problems in Rust is not anything to ignore. Rust is great for building tools like DataFusion, Polars, or delta-rs that can be the backbone of other data systems … but for everyday Data Engineering pipeline use? I have my doubts.

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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, doesn’t it?

Would anyone like a nice big slice of groupBy, maybe agg is what you need? No? Can you say distributed data set? Whatever it is you’re looking for, I’m quite sure a nice old DataFrame can give it to you. With so many options to choose from … what do you choose? I don’t know, whatever works best for you. But, it does set the stage nicely for a clash of the titans per see.

Let’s do this just that. Straight out of the box performance test. Bunch of CSV’s, a little aggregation, just some simple stuff. Mirror mirror on the wall, who is the fastest with DataFrames of them all?

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Delta Lake without Spark (delta-rs). Innovation, cost savings, and other such matters.

Photo by krakenimages on Unsplash

The intersection of Big Data and Not Big Data.

An interesting topic of late that has been rattling around in my overcrowded head is the idea of Big Data vs Not Big Data, and the intersection thereof. I’ve been thinking about SAAS vendors, the Modern Data Stack, costs, and innovation. A great real-life example of all these topics is Delta Lake. Delta Lake is the child of Databricks, officially or not, and at a minimum has exploded in usage because of the increasing usage of Databricks and the popularity of Data Lakes.

Delta Lake, Hudi, Iceberg, all these ACID/CRUD abstractions on top of storage for Big Data have been game changers. But, as with any new popular tech, it comes with its own set of challenges. Specifically for Delta Lake … if you want to use it 99.9% of people are going to have to use Spark to do so, which can be costly, in terms of running clusters, and add complexity, in terms of new tooling, data pipelines, and the like. Anytime you only have one path to take with a tool, innovation is stifled, and barriers arise. Enter delta-rs the Standalone Rust API for Delta.

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DataFusion courtesy of Rust, vs Spark. Performance and other thoughts.

Photo by Benjamin Wedemeyer on Unsplash

I think it’s funny that DataFrames are so popular these days, I mean for good reason. They are a wonderful and intuitive way to work with and on datasets. Pandas … the nemesis of all Data Engineers and the lover of Data Scientists. Apache Spark is really the beast that brought DataFrames to the masses. Even those little buggers over at Apache Beam give you DataFrames.

Of course, when anything gets popular, you start getting little things that start to pick and peck at the heels. I would probably say that is what DataFusion with Rust seems to be. Seems more like a contender against Pandas rather than Spark to me. I guess if you’re just using Spark locally or on a single node, sure you could consider using DataFusion. Code available on GitHub.

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Working with Cloud Storage (s3). Golang vs Rust vs Python. Who shall emerge victorious?

Photo by José Ramos on Unsplash

I’ve always been a firm believer in using the right tool for the job. Sometimes I look at a piece of code … and ask … why? I mean just because you can do something doesn’t mean that you should. I see a lot of my job as someone who writes code … as not just my ability to write code, but the ability to reason about problems and design simple and elegant solutions that solve the problem at hand.

I try not to let my love of a tool, language, or package color my view of the world as it is. In fact, there is wisdom to be found in being critical of those languages and tools you love the most. Be aware of their shortcomings and failures. This leads to better software and architecture designs, and less complexity. Too often I’ve seen folks picking their tool of choice and then sticking with it till the bitter end, and it usually is bitter. There is more to life than writing obtuse Scala code that is illegible for some mundane task.

This sort of thing is a blight on everyone and every system. Now I must descend from my high horse and join the peasants on the dusty road of life. Today I want to look at some very common Data Engineering tasks, namely cloud storage, and what it is like to do such a thing with Golang, Rust, and Python. I will let you draw your own conclusions. Maybe. Code available on GitHub.

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Thoughts on Saint Augustine, Rust vs Golang. Complexity, verbosity, and other matters.

Image: Saint Augustine of Hippo | Line engraving by P. Cool after M. de Vos | Wellcome Images

I’ve always enjoyed reading Mr. Augustine of Hippo, particularly “Confessions.” Ahead of his time in many ways. Although, you have to be into that sort of thing to find such topics interesting. It can be sort of dry, drawn out, verbose, and not for the faint of heart. Much like learning new programming languages. I’ve been messing with Golang off and on and here and there. Recently I added Rust to that list, more out of curiosity and to see what’s new in the world.

I’ve spent a lot of time thinking about the theology of programming in the space of Data Engineering. It’s such a wide area that encompasses so many different skills, Data Engineering that is. Why do we do what we do, write what we write? Like Augustine I see both old and new all around me, some things change, but many things stay the same.

People find hills like Python, Scala, Golang, Rust, and then promptly decide to die on them. I enjoy different things simply because of the way they teach you things about yourself and the world.

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