Learn how to reduce costs and improve query speed with these expert optimization strategies.
The Problem
Querying large tables without partitioning means every query scans the entire dataset, even if you only need a small slice of data.
The Solution
Partition your tables based on your most common query patterns:
CREATE OR REPLACE TABLE mydataset.partitioned_table PARTITION BY DATE(timestamp_column) AS SELECT * FROM mydataset.original_large_table;
By adding a WHERE clause that references your partition field, you only pay for the data you actually scan:
SELECT * FROM mydataset.partitioned_table WHERE DATE(timestamp_column) BETWEEN '2023-09-01' AND '2023-09-07'
Cost Impact: Partitioning can reduce query costs by 50-90% depending on your data access patterns.
2. Cluster for Additional Precision
Beyond partitioning, clustering helps organize related data:
CREATE OR REPLACE TABLE mydataset.partitioned_clustered_table PARTITION BY DATE(timestamp_column) CLUSTER BY user_id, region AS SELECT * FROM mydataset.original_large_table;
Pro Tip: Combine partitioning with clustering for maximum cost efficiency.
3. Embrace BigQuery’s Materialized Views
Materialized views store pre-computed results that automatically update when the source data changes:
CREATE MATERIALIZED VIEW mydataset.mv_daily_metrics AS SELECT DATE(timestamp) as day, COUNT(DISTINCT user_id) as unique_users, SUM(revenue) as total_revenue FROM mydataset.events GROUP BY 1;
BigQuery intelligently uses the materialized view even when you query the source table directly, potentially reducing both cost and computation time.
4. Utilize the BI Engine for Dashboarding
If you have dashboards that repeatedly query the same data, allocate a portion of BI Engine capacity to dramatically reduce costs for those specific tables.
Cost Impact: BI Engine can completely eliminate query costs for dashboard queries against the reserved tables.
5. Use Script Parameters for Query Development
When developing and testing queries, use script parameters to limit the data you’re scanning:
DECLARE start_date DATE DEFAULT '2023-09-01'; DECLARE end_date DATE DEFAULT '2023-09-03'; SELECT * FROM mydataset.events WHERE DATE(timestamp) BETWEEN start_date AND end_date LIMIT 1000;
This approach allows you to test query logic on small data samples before running against the full dataset.
6. Set Custom Cost Controls
Implement project-level daily quotas and user-level query limits:
- In the BigQuery console, go to “Resource Management” → “Custom Quotas”
- Set daily byte limits for projects or specific users
Pro Tip: Set up BigQuery monitoring to alert you before you hit cost thresholds.
7. Master the Art of Table Sampling
For exploratory queries where approximate answers are sufficient, use table sampling:
SELECT * FROM mydataset.huge_table TABLESAMPLE SYSTEM (5 PERCENT)
This samples approximately 5% of the data, reducing your query cost proportionally.
Conclusion
By implementing these seven strategies, many DataQueryLab clients have cut their BigQuery costs by 40-70% while maintaining or even improving their analytical capabilities.
Remember that BigQuery pricing is primarily based on data scanned, not stored. The most effective strategy is to minimize unnecessary scanning through good data modeling and query optimization.
What BigQuery cost-saving techniques have worked for your organization? Share your experiences in the comments below!
