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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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Replacing Pandas with Polars. A Practical Guide.

Photo by Stone Wang on Unsplash

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 first packages you use other than say … requests?

You know, Pandas feels like Airflow, everyone keeps talking about its demise, but there it is everywhere … used by everyone. Sure it’s old, wrinkled, annoying, slow, and obtuse, but it’s ours, and that makes it the words of Gollum … precious.

We should probably get to the point already. Everyone is talking about Polars. Polars is supposed to replace Pandas. Will it? Maybe 10 years from now. You can’t untangle Pandas from everywhere it exists overnight. Do you still want to replace Pandas with Polars and be one of the cool kids? Ok. Let’s take a look at a practical guide to replacing Pandas with Polars, comparing functionally used by most people. My code is available on GitHub.

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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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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 Delta Lake, lakeFS, Hudi, and the like.

Sure, these tools have been around for some time, but the uptake and adoption of them all have been rapidly growing. I use Delta Lake on a daily basis, taking advantage of the many wonderful features it provides to simplify and reduce complexity in data pipelines. But, I’ve been sitting around for a long time waiting for the plethora of “add-on” tooling to come out, stuff that will make my life easier. I recently saw one of the first tools like that for Delta Lake, namely mack.

Mack appears to have the ability to “do the hard work for you,” a concept that appears to be growing in popularity, but which I have a fraught relationship with. Double-edged sword? Let’s find out.

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

Photo by Jason Leung on Unsplash

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 data.

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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 up here and there and everywhere.

The only reason I know anything about Arrow is that some years ago, circa 2019 and earlier I stumbled into Arrow and used it to read and write Parquet files (pyarrow that is). Heck, I even used it to tie together Python and Hadoop, Lord knows what I was thinking back then. I’m amazed at how much I used PyArrow back in the day, even to compare Parquet vs Avro.

“Back then it seems like no one used Arrow much, no one was writing about it, using it, or talking about it. At least not that I saw. But oh how times have changed. Arrow seems to be showing up everywhere and is starting to become a backbone for many other tools.”

– me
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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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Why Data Migrations Suck.

Photo by Ray Hennessy on Unsplash

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 teams down to a crawl, take at least twice as long as planned, and be way more difficult than imagined.

Can’t it be made easy, shouldn’t Data Migrations have been conquered by now? I mean just put together the perfect plan, break up the work, make a bunch of tickets, estimate the work, and the rest falls into place? If only.

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A Tale of Betrayal and Heartbreak – Databricks Workflows and Jobs.

Photo by Francesco Alberti on Unsplash

Nothing captures the imagination and heart like a tale of betrayal and heartbreak, and that is a tale I want to bring to you today. It’s a tale of Databricks Workflows and Jobs, version changes, new features, API’s, and insidious little hidden gems that will make you pull your hair out when you find them. It’s a tale of what not to do, a tale of how to put developer and customer experience first, instead of forcing unwanted solutions down the throats of the little birdies feeding at your nest.

As a Data Engineering simplicity and ease of use is something close to my heart, something that Databricks did well, or maybe I should say used to do well … before recent releases like Jobs 2.1 API. I hope you can hear the bitterness oozing from my words.

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A Diatribe against Data Contracts and their Abuses.

Ok, so I don’t really mean all that. Or do I? I have no idea what the future holds. Sometimes it’s easy to pick out the winners, like Databricks and Snowflake, you can see, feel, and taste the results of those data products, a delicious and delectable bounty to feast upon. Other things are harder to read the tea leaves on. Kinda like Data Mesh … is it a thing, or is it not a thing? It’s hard to decern between charlatans and marketing/sales departments hocking the next Cure All Snake Oil and real life.

What about all this recent humdrum and buzz around Data Contracts? Pushed by some popular Data Engineering faces like Ananth Packkildurai and Chad Sanderson. What is all the hype about Data Contracts, are folks just pushing another tool down our throats? Is there a real issue and problem that can be solved with Data Contracts?

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