Mastering Cost Optimization in BigQuery

BigQuery's serverless model makes it easy to run analytics at scale — but at a cost. If not managed well, on-demand queries can lead to unpredictable bills and unnecessary compute waste.

This guide focuses on actionable ways to reduce BigQuery costs in real-world scenarios.

1. Use Partitioning and Clustering

• Partition by date for time-series data
• Cluster by frequently filtered columns This significantly reduces the amount of scanned data and speeds up queries.

2. Avoid SELECT *

Only select the columns you need. BigQuery charges based on the amount of data processed — not the number of rows returned.

3. Use Materialized Views and Cached Results

Leverage materialized views for repetitive, heavy queries. Enable query result caching to reuse previously computed results — free of charge.

4. Monitor Query Costs with INFORMATION_SCHEMA

Use BigQuery’s built-in metadata tables to:

• Audit query history
• Analyze usage trends
• Track high-cost queries by user/project

5. Estimate Before You Run

Use EXPLAIN and preview mode to estimate query cost and scanned bytes before execution.

6. Flatten Nested Data Efficiently

Denormalizing or flattening too aggressively can lead to data bloat. Use UNNEST() wisely and filter before you flatten.

Final Thoughts

With the right techniques, you can turn BigQuery from a flexible analytics tool into a cost-efficient, scalable powerhouse.

Efficiency in the cloud isn't about cutting usage — it’s about making every byte count.