Reducing BigQuery Costs by 260x

In this blog post, the author explores how clustering large tables in BigQuery can significantly impact costs. The use-case for a common query pattern (MERGE) is discussed, where clustering reduces the amount of data processed by BigQuery from 10GB to 37MB, resulting in a cost reduction of ~260X. By intelligently clustering tables on columns that are frequently used in join and WHERE clauses, significant cost savings can be achieved. The author also mentions how PeerDB automatically clusters and partitions raw and final tables on BigQuery, leading to 2x-10x cost reduction for their customers.

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