Preserving context history conflicts with stopping obsolete metric definitions from reaching warehouse queries.
Context Builder must keep customer records traceable while AISQL must stop producing credible answers from retired dbt definitions.
Who you’d be doing this for
“Finance asked why this week’s margin moved, and I had to tell them the definition might have changed underneath it.”
Elsa Jarvinen · Data Engineering Manager
Leads an enterprise data team that relies on dbt definitions and AISQL answers for finance reporting.
What is at stake
AISQL is returning warehouse-valid answers from retired metric definitions. You must weigh immediate containment against preserving the evidence needed for a trustworthy rebuild.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- live context freshness vs. semantic correctness
- history preservation vs. irreversible rebuild
- warehouse-valid SQL vs. trusted business meaning
- fast rollback vs. complete traceability
Why Fivetran
AISQL depends on dbt metric definitions and warehouse execution, so stale context turns a valid BigQuery query into an untrustworthy business answer.
Written with these in mind
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This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.