Explanation:
The correct answer is D. Partition the data by transaction date. Partitioning in BigQuery organizes your data into segments based on the transaction date, allowing queries to scan only the relevant partitions. This method optimizes query performance for specific date ranges without additional storage costs.
Partitioning by transaction date is the recommended approach to enhance query performance efficiently in BigQuery.
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Your company is moving its data warehouse to BigQuery, and you're responsible for designing the data model. After migrating an on-premises sales data warehouse with a star schema to BigQuery, you notice performance issues when querying the last 30 days of data. According to Google's best practices, what's the most effective way to improve query performance without increasing storage costs?
A
Combine tables and duplicate data to reduce joins (Denormalize the data).
B
Split the data based on customer IDs to distribute it across multiple nodes (Shard the data by customer ID).
C
Pre-compute and store the results of complex queries in views (Materialize the dimensional data in views).
D
Divide the data into smaller parts based on the transaction date (Partition the data by transaction date).