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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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Exploring Graphs in Rust. Yikes.

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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Conceptual Introduction to Delta Lake.

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Old Dog Learn New Tricks? Starburst (Trino) Galaxy and other thoughts.

Sometimes I think Data Engineering is the same as it was 10+ years ago when I started doing it, and sometimes I think everything has changed. It’s probably both. In some ways, the underlying concepts have not moved an inch, some certain truths and axioms still rule over us all like some distant landlord, requiring us to pay the piper at a moment’s notice. Still, with all those things that haven’t changed, the size, velocity, and types of data have exploded. Data sources have run wild, multiple cloud providers, and a plethora of tooling. 

So yes, maybe in a lot of ways Data Engineering has changed, or at least how we do something is a new and wild frontier, with beasts around every corner waiting to devour us in our ignorance. Never mind the wild groups of zealots roaming around seeking converts to their cause and spitting on those unwilling to bend.

Probably like many of you, I’ve had a healthy skepticism of all things new, at least until they have proved themselves out over some time. This is both a good and a bad habit. It can protect you from undo harm and foolishness, but can also be lost opportunity when you pass over the diamond in the rough. I for one, think that if something is worth its weight in salt, it is usually clear, and its obvious value can be discerned quite readily.

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4 Ways To Setup Your Data Engineering Game.

One of my greatest pleasures in life is watching the r/dataengineering Reddit board, I find it very entertaining and enlightening on many levels. It gives a fairly unique view into the wide range of Data Engineering companies, jobs, projects people are working on, tech stacks, and problems that are being faced.

One thing I’ve come to realize over the years, working on many different Data Teams, and backed up by a casual observation of discussions on Reddit and other places, is that despite us living in the age of ChatGPT, Data Engineering teams generally seem to lag far behind in most areas of the Development Lifecycle.

So, to fix all the problems in the entire world and save humanity and Data Engineers from themselves, I give you the gift of telling you how to do your job. You’re welcome.

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Polars – Laziness and SQL Context.

Polars is one of those tools that you just want … no … NEED a reason to use it. It’s gotten so bad, I’ve started to use it in my Rust code on the side, Polars that is. I mean you have a problem if you could use Polars Python, and you find yourself using Polars Rust. Glutton for punishment I guess.

I also recently took personal offense when someone at a birthday party told me that everyone uses Pandas, and no one uses Polars in the real world. Dang. That hurt.

The reality is that I know it takes a long while for even the best technologies to be adopted. Things don’t just change overnight. But there are two hidden gems of Polars that will hasten the day when Polars replaced Pandas for good. Let’s talk about them.

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Real Talk about Running Databricks + Delta Lake at Scale.

Anyone who’s been working in Data Land for any time at all, knows that the reality of life very rarely matches the glut of shiny snake oil we get sold on a daily basis. That’s just part of life. Every new tool, every single thingy-ma-bob we think is going to solve all our problems and send us happily into the state of nirvana inside our eternal data pipelines, is a lesson in disappointment.

I get it, there are a lot of nice tools out there. I use some of them every day. But, a healthy dose of reality is good for us all. Don’t lie to yourself. There is no such thing as the perfect tool. There are good tools, bad tools, and tools in between. The Truth is that all tools get pushed to their limits at some point.

We work on small teams, we don’t have all the time in the world, and we have to deliver our data at some point, perfect or not. We cut corners, hopefully, the right ones. That’s part of being wise and putting years of data experience to work. Today I’m going to talk about my experience of running Databricks + Delta Lake at scale. What happens when you use Databricks to ingest and deal with 10’s of millions of records a day, billions+ records a month?

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DuckDB vs Polars for Data Engineering.

I was wondering the other day … since Polars now has a SQL context and is getting more popular by the day, do I need DuckDB anymore? These two tools are hot. Very hot. I haven’t seen this since Databricks and Snowflake first came out and started throwing mud at each other.

You might think it doesn’t matter. Two of one, half-dozen of another, whatever. But I think about these things. Simplicity is underrated these days. If you have two tools but could do it with one, should you use two? Probably depends on the Engineering culture you’re working in.

I mean just because you can doesn’t mean you should. Some data engineering repo with 50 different Python pip packages installed, constantly breaking and upgrading for no reason. CI/CD build failing, conflicts. Frustration. Why? Just because someone wants to do this one thing and decided they needed yet another package to do it.

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The Dog Days of PySpark

 

PySpark. One of those things to hate and love, well … kinda hard not to love. PySpark is the abstraction that lets a bazillion Data Engineers forget about that blight Scala and cuddle their wonderfully soft and ever-kind Python code, while choking down gobs of data like some Harkonnen glutton.

But, that comes with a price. The price of our own laziness and that idea that all that glitters is gold, to take the easy path. One of the main problems is the dreadful mistake of mixing native Python in with your PySpark and expecting things to go fine at scale. Which it most assuredly will not.

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Polars vs Spark. Real Talk.

Real talk. Polars is all the rage. People love Spark. People use Spark for small data, but data is too big for Pandas. Spark runs on a local machine. Polars runs on a local machine. What do I choose, Spark or Polars? Does it matter?

I’ve written about Polars at different points, here, and here when discussing wider topics. I mean honestly, I think Polars is the best tool to come out in the last 5 years of Data Engineering. But I find it unwaveringly boring. Which is why it’s so popular.

It’s boring for anyone who has used Pandas, Spark, or other Dataframe tools a lot. Sure, it can be a cool breeze in the face of some poor sap who’s been chained down to Pandas by some boss hanging around from a bygone era. You know what I’m talking about.

But honestly, overall, if you’re just an average engineering piddling around with datasets on your machine, what should you choose? Spark or Polars. Let’s talk some real talk.

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