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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
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?
ABecause increasing compute power automatically optimizes queries
BBecause reducing data scanned lowers cost directly, while more compute power costs more money
CBecause filtering data increases storage costs
DBecause selecting more columns reduces query speed
Step-by-Step Solution
Solution:
  1. Step 1: Understand cost model of warehouses

    Costs depend on data scanned and compute time; more compute power costs more money.
  2. Step 2: Compare filtering/selecting columns vs increasing compute

    Filtering and selecting reduces data scanned, lowering cost directly; increasing compute power raises cost.
  3. Final Answer:

    Because reducing data scanned lowers cost directly, while more compute power costs more money -> Option B
  4. Quick 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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