dbt - Performance Optimization
You want to optimize a large fact table in Snowflake using dbt. Which combination of warehouse-specific optimizations is best to improve query speed and reduce costs?
You want to optimize a large fact table in Snowflake using dbt. Which combination of warehouse-specific optimizations is best to improve query speed and reduce costs?
hard🚀 Application Q15 of 15
Step-by-Step Solution
Solution:
Step 1: Identify best practices for large Snowflake fact tables
Clustering keys on date columns improve pruning and query speed; incremental with merge avoids full reloads.Step 2: Evaluate options
Use clustering keys on date columns and incremental materialization with merge strategy combines clustering and incremental with merge, which is optimal. Others miss clustering or unique_key or use inefficient materialization.Final Answer:
Use clustering keys on date columns and incremental materialization with merge strategy -> Option BQuick Check:
Clustering + incremental merge = best for large tables [OK]
Quick Trick: Combine clustering and incremental with merge for big tables [OK]
Common Mistakes:
MISTAKES- Skipping clustering on large tables
- Using full refresh unnecessarily
- Omitting unique_key in incremental
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