Analytics engineering is messy and iterative by nature - you need to tweak the logic, change the metric definition and try again.
Yet we often run this entire process directly on full datasets.
You usually don't need all the data
Most of the time, you're not testing scale to begin with. You're testing logic - does the join work? does this filter do what I think it does? does this metric actually make sense?
For that, a small, truly random sample is more than enough.
With a good sample, you can move fast:
- •Queries run instantly
- •You can iterate without worrying about cost
- •You're more willing to experiment
Running everything on the full dataset just slows you down and makes you second-guess changes.
A sandbox is not "dev but smaller"
A real sandbox should be cheap, fast, and easy to throw away. It's the place where you:
- •Try new ideas
- •Break things on purpose
- •Ask "what if?" without consequences
Most teams skip this step and jump straight from idea to production logic. That's why analytics work often feels heavier than it needs to be.
This hurts small teams the most
If you're a small team, you don't have time or budget to run expensive experiments.
So you either:
- •Test directly in production, or
- •Avoid experimenting altogether
And neither of these options sound great.
How we think about this at Yorph
At Yorph, we treat sandboxing as a normal part of analytics work.
Instead of spinning up heavy environments, you can just ask for a small, random dataset and start iterating. The goal isn't to replace production testing but to get the logic right before production is even involved.
Analytics engineering needs space to explore. Small, random sandbox datasets make that possible - and we should be using them way more than we do.