How can you optimize performance in BigQuery?

 

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To optimize performance in BigQuery, follow these key strategies:

Partitioning & Clustering: Partition large tables by date or integer fields to reduce scanned data. Cluster tables on frequently filtered columns to improve query speed.

Use SELECT Only Needed Columns: Avoid SELECT *. Fetch only required columns to reduce data scan and cost.

Filter Early: Apply filters in WHERE clause as early as possible to limit rows processed.

Use Materialized & Cached Views: Materialized views store precomputed results. Repeated queries benefit from cache and materialized data.

Avoid Cross Joins: Use proper join conditions. Cross joins without filters lead to exponential growth in data processed.

Sharded vs Partitioned Tables: Use partitioned tables instead of creating multiple sharded tables (e.g., table_20240101).

Query Execution Plan: Use EXPLAIN to analyze and refine execution steps.

Denormalization: Flatten data where appropriate to reduce complex joins.

Limit & Preview: Use LIMIT and TABLESAMPLE when testing queries.

By combining these practices, you can significantly reduce costs and improve BigQuery query performance.

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Explain GCP IAM roles.