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Answer: Refrain from using `SELECT *` to avoid scanning unnecessary data, Implement clustered tables to minimize data scanned
To effectively reduce BigQuery costs, it's crucial to avoid scanning excessive data. Using `SELECT *` can lead to scanning more data than necessary, thus increasing costs. Clustered tables help in organizing data more efficiently, reducing the amount of data scanned during queries. The `bq --dry_run` command is recommended for estimating query costs, not `--estimate_bytes`. Partitioning and clustering are both beneficial for limiting scanned data and reducing costs. However, using `LIMIT` on non-clustered tables does not reduce the amount of data scanned. For more details, refer to [BigQuery best practices for performance](https://cloud.google.com/bigquery/docs/best-practices-performance-overview).
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To optimize BigQuery usage and reduce costs for data analysts, which two practices should you recommend?
A
Utilize the bq --dry_run command to estimate query costs before execution
B
Implement clustered tables to minimize data scanned
C
Refrain from using SELECT * to avoid scanning unnecessary data
D
Limit the use of partitioned tables to reduce complexity
E
Apply LIMIT clauses exclusively to non-clustered tables for efficiency