How to Optimize Google Cloud BigQuery and Control the Cost

The article discusses how a developer unknowingly incurred $3,000 in costs while using Google Cloud BigQuery due to a poorly optimized query.

The article outlines three easy steps to optimize the query and reduce costs by 99.97%.

These steps include avoiding using "SELECT *", using a partitioned table and querying only subsets of data, and using materialized query results in stages. The article also dives into BigQuery's architecture and underlying computing engine, Dremel, providing useful references for further reading.