Google BigQuery is the undisputed titan of serverless data warehousing, but its “pay-as-you-go” flexibility is a double-edged sword. In 2026, as data volumes explode and AI-driven queries become the norm, a single unoptimized SELECT * on a petabyte-scale table can cost more than a mid-sized car.

If your Google Cloud bill is climbing, you aren’t alone. However, ranking in the top tier of data-driven companies requires more than just processing power; it requires fiscal efficiency. This guide breaks down seven expert strategies to optimize your BigQuery performance while keeping your costs firmly on the ground.

1. Stop the “SELECT *” Habit
The most common cause of budget overruns is scanning more data than necessary. Because BigQuery uses a columnar storage format, it only charges for the columns you actually query.

The Fix: Always explicitly name your columns.
The Pro Tip: Use the Dry Run feature in the Google Cloud Console or CLI before hitting “Run.” It calculates the bytes processed without costing you a cent.

2. Implement Partitioning and Clustering
If you are querying a table with five years of data but only need the last seven days, BigQuery shouldn’t have to scan the whole thing.

Partitioning: Divides your table by segments (usually DATE or TIMESTAMP).
Clustering: Sorts data based on specific columns (like user_id).
When combined, these can reduce query costs by over 90% for large datasets.

3. Leverage BigQuery BI Engine
For dashboards (Looker, Tableau, Power BI), frequent small queries can add up. BigQuery BI Engine is an ultra-fast, in-memory analysis service. By caching frequently accessed data, it not only accelerates your dashboards to sub-second speeds but also reduces the number of slots consumed, lowering your overall compute spend.

4. Move from On-Demand to Capacity Pricing
For startups, On-Demand pricing ($6.25 per TB as of 2026) is great. But for enterprises hitting 50k+ monthly queries, Capacity-Based Pricing (Slots) is the way to go.

Note: Use Autoscaling Slots to handle peak loads without over-provisioning.

5. Use Materialized Views
If you find yourself running the same aggregation (e.g., daily revenue totals) repeatedly, stop wasting money. Materialized Views pre-compute these results in the background. Unlike standard views, BigQuery only processes the changes in the base table, making them incredibly cost-efficient for real-time analytics.

6. Optimize Your Joins
The “Large-into-Small” rule still applies in 2026. When joining tables, always place the largest table first. This allows BigQuery to broadcast the smaller table to all slots, preventing “Data Shuffle” bottlenecks that spike compute costs and slow down your pipeline.

7. Monitor with Information Schema
You cannot manage what you do not measure. Use BigQuery INFORMATION_SCHEMA to identify your most expensive “Top 10” queries and the users running them.

Frequently Asked Questions (FAQ)

How do I set a BigQuery budget alert?
Navigate to the Billing section of the Google Cloud Console. You can set “Pub/Sub” notifications to trigger a script that kills long-running queries if they exceed a daily spend threshold.

Is BigQuery Storage expensive?
Actually, BigQuery storage is very cheap. If a table isn’t edited for 90 days, Google automatically drops the price by 50% (Long-term storage), making it comparable to Google Cloud Storage (GCS).

Does “LIMIT 10” save money?
No. In BigQuery, a LIMIT clause does not reduce the amount of data scanned; it only limits the results displayed. To save money, use WHERE clauses on partitioned columns.

Conclusion: The Path to 2026 Data Efficiency

Optimizing BigQuery isn’t a one-time task — it’s a culture. By implementing partitioning, monitoring your slots, and ditching SELECT *, you can transform your data warehouse from a cost center into a high-speed engine for growth.