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A company uses an extract built from Custom SQL joining Claims and Members.
Members have multiple records in both tables causing data duplication, which results in inflated claim cost trends.
Which approach meets performance and maintenance goals?
Comprehensive and Detailed Explanation From Exact Extract:
The problem:
Custom SQL joins two multi-row tables, causing many-to-many duplication.
This artificially multiplies claim costs.
The extract becomes heavy and slow due to Custom SQL.
Tableau's recommended solution:
Use Relationships in the Logical Layer
Instead of physical joins
Tableau resolves many-to-many issues automatically
Query is generated at the appropriate granularity to avoid duplication
This is exactly Option A.
Relationships allow the Claims facts to remain at the claim grain and Members to remain at the member grain. Tableau resolves aggregations correctly, preventing inflated values.
Why the others are incorrect:
B --- Physical Join
Would continue the same duplication problem because multi-row joins multiply rows.
C --- LODs
Would require complex calculations and are error-prone.
They do NOT fix the duplication in the underlying extract.
D --- Table Calculations
Happen after Tableau aggregates the duplicated data --- too late to fix the inflated baseline numbers.
Thus, the only correct and modern solution is relationships.
Relationships documentation explaining resolution of many-to-many granularity issues.
Guidance recommending avoiding Custom SQL for performance reasons.
Logical Layer behavior preventing row-duplication errors.