Corporate for "I forgot what this is about but I need to make noise before someone notices".

An interactive report that executives will ignore until they ask for the same data… in an Excel sheet.

Technologically frozen in 1995. Still thinks "the cloud" is for rain and refuses to click anything newer than Solitaire.

Turning data into a fixed-size mess—useful for passwords, not so great if you ever need to reverse it.

Proof that "we'll fix it later" never actually means later.

A passive-aggressive way to say “this will be your problem soon.”

How much pain your system can handle before collapsing.

Making sure your servers aren’t crying for no reason.

A fancy word for "number we use to see if our model sucks or not."

Guessing with data—because flipping a coin isn't "data-driven."

A statistical way to check if two things are related or if your data is just messing with you.

Telling you whether your results matter or if they’re just a fluke—like winning the lottery.

The underappreciated hero who turns messy data into charts and makes everyone else look good.

Helping engineers understand how data flows, transforms, and actually works.

That thing developers ignore until the database breaks.

Data about your data—because keeping track of what your numbers mean is harder than it should be.

Data that refuses to fit into neat tables—think text, images, and the chaos of the internet.

Lives in a command line, thrives in mayhem. Breaks things just to make them better. Somehow delivers magic at 2 AM.

“We’ll consider all possible factors… except the ones that make us look bad.”

A free tool for tracking website traffic—until privacy laws step in.

Redefines success metrics faster than politicians backpedal after an election.

Google's open-source machine learning library—great for deep learning, if you don’t mind the steep learning curve.

Learned SELECT * yesterday and now wants database admin privileges – what could go wrong?

The behind-the-scenes details of how data was collected.

Workflow automation, so you don’t have to babysit data pipelines.

The dashboards and reports that will be outdated within a week.

The easiest SQL query that someone still wants to call a "data-driven insight."

The serial focus assassin. Everyone knows at least one.

The buzzword architects love, but engineers fear.

Modeled after the human brain, but way less reliable at common sense. Great at deepfakes, though.

Because SQL SELECT wasn’t fancy enough.

Splitting your database into smaller disasters.

The law that keeps finance teams on their toes.

The Data Lake’s evil twin.

A vague, last-minute ask that will inevitably require multiple follow-ups and scope changes.

A/B testing’s overachieving cousin.

The thing everyone builds but nobody documents.

When your data is so bloated no one knows what to do with it, but it sounds impressive.

The terrifying process of taking your machine learning model from theory to the real world, where it can finally embarrass you.

“We need better numbers, but we don’t want to change anything.”

Hiding sensitive data so developers don’t see what they shouldn’t.

Your code, but only when someone remembers it exists.

A checklist of rules to follow… until regulations change again.

Fake data used for training models when real data is too sensitive, messy, or non-existent.

Granting permissions based on job roles, not personal favorites.

Spotting the oddballs in your data, because sometimes anomalies are fraud, and sometimes they’re just mistakes.

XML’s cooler, slightly less annoying cousin.

Because just because you can collect data doesn’t mean you should.

“This dashboard is broken, but let’s not discuss it in front of leadership.”

A last-minute meeting because someone didn’t read the dashboard.

Sharing resources and pretending everything is fine.

The fine art of deciding who gets in and who gets a "403 Forbidden."

The bare minimum dressed up like a competitive edge.

Making your inefficient queries slightly less embarrassing.

Stripping personal details so data looks anonymous (but isn’t always).

Because winging it with data governance isn’t a long-term strategy.

Because sometimes, you actually want long-winded responses.

“I don’t trust your analysis, so let’s keep poking at it until it fits my narrative.”

Would slap glitter on a bankruptcy report because "data doesn't pop without gradients!"

“Throw some data models at the wall and see what sticks.”

Because reading rows one at a time is for chumps.

This query better finish before the meeting, or I’m in trouble.

No one understands the report, but we’re pretending we do.

Fancy PowerPoint slides no one follows.

When your company trends on Twitter for all the wrong reasons.

Translating raw data into real-world meaning so it’s actually useful.

The frustrations of explaining, again, why two reports don’t match.

“Let’s keep slicing the data until we find something that supports our assumption.”

Tweaking your dataset to improve model performance—because sometimes you need to cheat a little.

The family tree of your data, assuming you can track it.

When bad data leads to even worse decisions.

Because bad data leads to bad decisions and lots of excuses.

Proof that a company probably takes security seriously.

Absolute chaos agents.

When everyone agrees on what to pretend to care about.

Because not every department deserves full database access.

Deploying apps without touching infrastructure (until something breaks).

Because someone needs to process transactions in real-time.

When your model is too smart for its own good and memorizes the training data instead of learning useful patterns.

Data’s glow-up into something actually useful.

The reason your computer fan sounds like a jet engine.

Making sense of numbers so businesses can pretend to be data-driven.

Making sure your app doesn’t make users want to throw their devices.

Tweaking a button color and calling it "strategy."

Rules everyone agrees on but nobody follows.

When search meets machine learning and everyone gets confused.

The dashboard everyone ignores until an executive asks for it.

Renting someone else’s servers but paying more.

Keeping unauthorized users out - until someone shares a password.

Load first, transform later—modern data integration in action.

Making sure your data descriptions don’t live in someone’s forgotten spreadsheet.

Making database queries run faster—because no one likes waiting 10 minutes for an SQL query to finish.

Because manually checking your code is for the weak.

“I have 10 dashboards to fix and zero time for your ad-hoc request.”

Because mistakes were made.

Nesting IF statements like Russian dolls and defending their desktop spreadsheet hoard like a caffeinated dragon.

Spotting the weirdos in your data—because outliers can mean fraud, errors, or just bad luck.

Pay a monthly fee to lose your files in someone else’s basement.

The lazy friend of machine learning—it just looks at its closest neighbors and copies them.

Blueprints for security that companies try to follow.

A fancy term for “don’t let hackers steal our stuff.”

Talking to inanimate objects because humans are worse.

Predicting trends over time—useful for stocks, weather, and figuring out when your Wi-Fi will crash again.