Here's something nobody tells you when you start in data engineering: data goes bad.
Not like "error 500" bad. More like "left the milk out" bad. Silent. Subtle. Dangerous.
I learned this the hard way. Built a beautiful pipeline once: clean transformations, perfect schema, the works. Proud of myself. Real proud. Then, two weeks later, predictions started drifting. Turns out the upstream API had quietly started serving cached responses after an update. My data looked fresh. Smelled fresh. But it was last week's data wearing a new timestamp like a cheap disguise.
That's the thing about data. It has a shelf life. And we keep pretending it doesn't.
Your customer segmentation from Q2? Adorable. Your users moved on in Q3. That fraud detection model from last year? Cute. Fraudsters iterate faster than your sprint cycle.
So now I check expiration dates like I'm grocery shopping. When was this actually generated? Has the schema shifted? Is the distribution what my model expects? Can I trace it back to source?
Because here's the truth: data without a valid date isn't data. It's a story you're telling yourself. And in production, self-deception is the most expensive bug you'll ship.