When data teams struggle with relationship discovery, the instinctive response is often:
“We’ll just analyze it manually.”
After all, engineers have SQL.
They can inspect schemas.
They can run queries.
They can sample data.
Surely that should be enough.
It isn’t.
The Pairwise Comparison Explosion
Let’s start with the math most teams never explicitly write down.
If you have:
· 100 tables
· each with 50 candidate fields
That’s 5,000 fields.
To check potential relationships, you’re not comparing tables—you’re comparing fields.
That means:
5,000 × 5,000 = 25 million possible comparisons.
Even if you reduce the search space aggressively, the numbers remain brutal.
And this isn’t theoretical.
Large enterprises routinely operate with tens of thousands of fields across systems.
Manual exploration simply cannot keep up with the combinatorial growth.
Why Brute-Force Comparison Is Infeasible
The naive approach is obvious:
“Let’s compare values between fields and see what matches.”
At small scale, this works.
At real scale, it collapses.
· Full scans are expensive
· Distinct values explode
· Network and compute costs spike
· Execution time becomes unpredictable
In many environments, full comparison is not just slow—it’s operationally impossible.
This is why teams quietly avoid doing it, even when they know it would be “more correct.”
The False Promise of “Just Sample Manually”
To cope, teams compromise.
They sample.
They grab 100 rows.
Or 1,000.
Or “whatever feels representative.”
Then they eyeball overlaps.
This feels pragmatic—but it introduces a new class of problems.
· Small samples miss rare but critical relationships
· Coincidental overlaps look meaningful
· Negative results are inconclusive
· No one knows when confidence is justified
Manual sampling has no statistical grounding, no repeatability, and no defensible stopping point.
It replaces computational limits with human bias.
SQL Is Powerful — But It’s the Wrong Abstraction
SQL is exceptional at executing known logic.
It is not designed to discover unknown structure.
Relationship discovery asks questions like:
· Which fields might be related at all?
· What kind of relationship is this?
· How strong is the signal?
· Is this inclusion, equivalence, or coincidence?
These are not query problems.
They are inference problems.
Trying to solve them with ad hoc SQL is like using a spreadsheet to reverse-engineer a compiler.
Relationship Discovery Is an Algorithmic Problem
At scale, relationship discovery requires:
· Feature extraction, not raw comparison
· Intelligent pruning, not brute force
· Probabilistic reasoning, not intuition
· Repeatability, not one-off analysis
This is why manual approaches fail—not because engineers are inefficient, but because the problem itself belongs to a different class.
Until teams treat relationship discovery as an algorithmic capability rather than a manual task, SQL will remain a debugging aid—not a solution.
And the guessing will continue.
Top comments (1)
Some comments may only be visible to logged-in visitors. Sign in to view all comments.