Stage Large Queries Instead of Being Clever
Intermediate tables are an architecture pattern, not a defeat.
Evidence tier describes my hands-on relationship with the content. Official sources describe platform contracts/documentation. Production observations should not automatically be treated as official limits.
When one Query Activity has too many joins, scans and transformations, split the work into deterministic stages. You gain observability, smaller scans and easier failure isolation.
One stage should have one obvious responsibility.
Use Overwrite for disposable staging tables where appropriate.
Keep downstream contracts explicit.
Measure runtime per stage instead of treating the automation as one black box.
Why this matters in SFMC
Query Activities have a 30-minute hard runtime limit. Salesforce also publishes soft limits around joins and the number of Data Extensions operated on in one query.
Salesforce recommends 3 joins or fewer for best performance, even though 4 joins is the published soft limit.
A practical shape
Instead of one huge tracking query, create Jobs → Opens → Subscribers → Final Target stages. That mirrors Salesforce’s own intermediate-table optimization example.
Operational upside
A failed stage tells you where the problem is. You can inspect the intermediate DE, validate row counts, and rerun only what is necessary.