Our monthly BigQuery bill is skyrocketing as our data warehouse grows. We are currently using the on-demand pricing model and running complex JOINs across several large tables. Are there specific partitioning or clustering strategies that can help prune the data scanned? Also, would switching to capacity-based (slots) pricing be more cost-effective at this scale?
3 answers
At 100TB, you must implement Partitioning and Clustering immediately. Use partitioning on a timestamp or date column to ensure queries only scan relevant days of data. Clustering on frequently filtered columns (like user_id or region) further organizes data within those partitions, significantly reducing the "bytes billed." Also, stop using SELECT * and only call the columns you need. Regarding pricing, if your baseline slot usage is consistently high, capacity-based pricing with "Autoscaling Slots" provides a more predictable monthly spend compared to the unpredictable nature of the on-demand $5 per TB model.
Are you currently using Materialized Views for those complex JOINs to avoid re-calculating the same data frequently?
Check your "Long-term Storage" pricing too. Data not edited for 90 days drops in price by 50% automatically, which helps if you have historical logs.
Emily is right. We saved a ton by moving our old log data to separate datasets that we rarely touch, letting the automatic discount kick in.
Christopher, we haven't tried Materialized Views yet because I was worried about the refresh costs. Does the background maintenance cost outweigh the query savings for datasets that update every hour? Actually, for hourly updates, BigQuery's smart tuning usually handles this efficiently. You’d save more on the repetitive JOINs than you'd spend on the incremental refreshes.