Transitioning to Senior Engineer

It’s probably what every single person wants to accomplish first after they’ve been writing code for a year professionally. How do I get to Senior Engineer? What skills do I need? “I am a good coder, give me the Senior Engineer title.”

Sadly, most Junior and Mid-Level Engineers think that being a Senior Engineer is all about coding, when in fact, it’s not. Being good at writing code is only the first step, the are many other way harder skills to learn to grow from Junior to Senior Engineer.

Databricks Doubles Cost. Reddit Explodes. I’m in Trouble!

I recently did a post on Linkedin and Reddit about Databricks removing Standard Tier and forcing folks into Unity Catalog. The post got big traction and blew up, more than I thought. Enough for the Databricks folk to hunt me down at work and tell me I’m naughty.

I will be writing a more in-depth post soon on Substack about the downsides of Vendor Lockin and how Data Teams should think about such things.

The Path To Senior Engineer

Want to know how to grow to the Senior Engineering position? Take a look.

Don’t Be So Smart

Most Software Engineers think of themselves as too smart. They think they are the best and brightest coder alive or that has ever lived. Doing so, they stunt themselves from becoming Senior Engineers and become hard to work with, the nightmare of the PR process.

You don’t need to be the smartest person in the room.

The Best Piece of Software Engineering Advice

You probably think this is another internet clickbait title uh? Just trying to get you to clickty clickty and sell you some Google Ads. Two problems. I don’t have Google Ads, and I know a small percentage of people will actually listen to this advice. Whatever. There is a reason some developers struggle to move past the Senior role.

Instead of making you scroll to the bottom to get what you can for, I’m going to give it to you off the top. How nice of me.

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The Abstraction Problem – A Great Evil

There is a great evil Spirit that is haunting the streets of code in the land of programmers. It’s a Spirit of obfuscation and twisting things into what they are not. The Spirit wanders around on the loose looking for someone, and it finds ready victims among the ranks of new programmers and the innocent young minds in University. It also finds a few old wizened souls that have been lost wandering for decades around the halls of some musty Fortune 500 company.

It’s the Abstraction Problem.

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How to be Better Than Everyone Else

Ok. Get off your high horse. You are human just like the rest of us. Just like your ancient ancestors who were throwing rocks and sticks at each other a thousand years ago … you are looking for a leg up on the competition. Isn’t that the world we live in? At the end of the day, no one is looking out for you, besides you.

Do you know what all those famous “influencers” have over you? Those you look at with boiling bitterness buried somewhere inside you. Besides a smattering of luck here and there, there is one other trait they have that most people don’t.

There are general branches and streams of actions that come off that main branch. But without that central drive, all else is useless.

What is it that apparently 1% of the data population has that the 99 don’t? Simply put, in the words of your grandparents, hard work.

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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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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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