This section walks through the idea with a short example, then the trade-offs you should mention in an interview.
In practice you start from the raw rows, apply the transform step by step, and check the shape of the result before you move on.
A common mistake is to jump straight to the final query without naming the grain or the join keys that keep the result correct.
Once the core path works, you harden it for nulls, duplicates, and late data so the pipeline stays reliable under load.
The Pro write-up covers the full explanation, worked examples, and the code you can run in the studio.
# Locked example
result = transform(frame)
print(result.head())
