dbt - Incremental Models
You have a large dataset with frequent updates and deletes. You want to optimize your dbt incremental model for performance and data accuracy. Which combination of strategies or techniques is best?
You have a large dataset with frequent updates and deletes. You want to optimize your dbt incremental model for performance and data accuracy. Which combination of strategies or techniques is best?
hard📝 Application Q9 of 15
Step-by-Step Solution
Solution:
Step 1: Analyze dataset needs
Frequent updates and deletes require handling all changes accurately.Step 2: Evaluate strategies
Append cannot handle deletes; merge handles updates/inserts but not deletes; delete+insert handles updates, inserts, and deletes.Step 3: Optimize performance
Using partition pruning with delete+insert reduces data scanned and improves speed.Final Answer:
Use delete+insert strategy with unique_key and partition pruning -> Option AQuick Check:
Delete+Insert + partition pruning = best for updates and deletes [OK]
Quick Trick: Combine delete+insert with partition pruning for large changing data [OK]
Common Mistakes:
MISTAKES- Using append when deletes exist
- Ignoring partition pruning for performance
- Mixing full refresh with incremental without plan
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