Скачать или смотреть When SQL FILTERS BACKFIRE

When SQL FILTERS BACKFIRE, the query still runs.
That’s the problem.

Most data mistakes don’t come from broken SQL.

They come from perfectly valid filters applied with the wrong mental model.

I’ve seen teams spend weeks debating metrics that were never wrong in the database, only wrong in intent.

A few common ways this happens:

A `WHERE` clause turns a `LEFT JOIN` into an accidental `INNER JOIN`.

Suddenly “missing” customers look like churn.

A date filter is applied AFTER aggregation instead of before.

Now growth is “real,” but only because half the history was quietly excluded.

A status flag meant for operational cleanup gets reused for analytics.

The number drops.
Everyone celebrates.
Nothing actually changed.

The query is clean.
The logic is not.

Filters feel harmless because they’re small.
Just One line.
One condition.

But every filter encodes a business decision:

Who counts
When something exists
What “active” really means

If that decision isn’t explicit, SQL will happily make it for you.

The most dangerous dashboards I’ve seen weren’t built by juniors.

They were built by experienced people moving fast, reusing patterns, trusting muscle memory.

The fix isn’t “write better SQL.”

It’s to slow down at the boundary between data mechanics and business meaning.

Before adding a filter, I ask one question:

“What story disappears if this condition is wrong?”

If you can’t answer that, the filter doesn’t belong in production.

Because misleading data rarely comes from BAD queries.

It comes from confident ones!!

If you want to build this foundation properly,
I’ve put together a FREE 7-day SQL Foundational Blueprint
focused entirely on the base of this pyramid.

LINK IN BIO🔗