dbt - Performance Optimization
Why does optimizing dbt models by filtering and selecting only necessary columns reduce warehouse costs more effectively than just increasing compute power?
Why does optimizing dbt models by filtering and selecting only necessary columns reduce warehouse costs more effectively than just increasing compute power?
hard🧠 Conceptual Q10 of 15
Step-by-Step Solution
Solution:
Step 1: Understand cost model of warehouses
Costs depend on data scanned and compute time; more compute power costs more money.Step 2: Compare filtering/selecting columns vs increasing compute
Filtering and selecting reduces data scanned, lowering cost directly; increasing compute power raises cost.Final Answer:
Because reducing data scanned lowers cost directly, while more compute power costs more money -> Option BQuick Check:
Reduce data scanned beats more compute = B [OK]
Quick Trick: Lower data scanned beats more compute cost [OK]
Common Mistakes:
MISTAKES- Thinking more compute optimizes queries
- Believing filtering increases storage cost
- Assuming selecting more columns speeds queries
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