How to Reduce BigQuery Costs Without Sacrificing Performance

Learn how to reduce BigQuery costs with practical strategies like partitioning, clustering, query optimization, and materialized views. Improve performance without increasing spend.

Shreya Banker

Data Scientist

Data Analyst enthusiast. More than 7 years of exposure in Data Analysis and Software programming. I am a highly motivated, versatile IT professional with experience in Data Analysis, Visualization and Database Management. I look for the hardest problem to solve and where I can learn and develop the most. I love a challenge and never run from a difficult task. I'm determined to succeed, and I look forward to what life has to offer.

BigQuery is an incredibly powerful analytics engine, but costs can quickly increase if it is not used efficiently. If you are not intentional with how queries are written and data is structured, you may end up paying significantly more than expected.

The good news is that with a few practical BigQuery cost optimization strategies, you can reduce query costs without sacrificing performance or flexibility.

Here is a practical, experience-backed guide to help you optimize your BigQuery usage.

Avoid Scanning Unnecessary Data in Queries

One of the most common and costly mistakes in BigQuery is querying more data than needed. Using SELECT * may feel convenient, but it is almost always inefficient.

Instead, be intentional:

-- Avoid thisSELECT * FROM `your_project.dataset.events`;-- Use thisSELECT event_name, user_pseudo_idFROM `your_project.dataset.events`WHERE event_date >= '20240301';

Why it matters:
BigQuery charges based on the amount of data processed. By selecting only the required fields and applying filters early, especially on partitioned fields like event_date or specific event names such as purchase or session_start, you significantly reduce the amount of data scanned and lower costs immediately.

Use Partitioning and Clustering to Reduce Costs

Partitioning and clustering are foundational BigQuery best practices for improving performance and reducing costs.

Partitioning divides tables into smaller segments, often by date, so queries scan only relevant data
Clustering organizes data by specific columns, making filtering and joins more efficient

Example: Create a Partitioned Table

CREATE TABLE `your_project.dataset.events_partitioned`PARTITION BY event_dateAS SELECT * FROM `your_project.dataset.events`;

Why it matters:
When queries filter on event_date, BigQuery scans only the relevant partitions instead of the full dataset. This directly reduces query cost and improves performance.

Use Approximate Functions for Large Datasets

For large-scale reporting where absolute precision is not required, BigQuery provides approximate functions that are faster and more cost-efficient.

Instead of:

SELECT COUNT(DISTINCT user_pseudo_id)FROM `your_project.dataset.events`;

Use:

SELECT APPROX_COUNT_DISTINCT(user_pseudo_id)FROM `your_project.dataset.events`;

Why it matters:
Approximate functions are significantly faster and typically accurate within 1 to 2 percent. This level of accuracy is sufficient for trend analysis and directional insights.

Important:
Approximate results should be used for reporting trends, not for calculations that require exact values, as small errors can compound.

Use Cached Results and Materialized Views

BigQuery automatically caches query results for 24 hours. Running the same query again within that timeframe does not incur additional cost.

To take advantage of this:

Avoid unnecessary query changes such as formatting differences or comments
Use materialized views for frequently executed queries

Example: Create a Materialized View

CREATE MATERIALIZED VIEW `your_project.dataset.user_summary`ASSELECT user_pseudo_id, COUNT(*) AS total_eventsFROM `your_project.dataset.events`GROUP BY user_pseudo_id;

Why it matters:
Materialized views store precomputed results. Instead of processing raw data repeatedly, BigQuery queries the stored output, reducing compute cost and improving performance.

5. Be Smart About Nested and Repeated Fields

BigQuery supports nested and repeated fields, which are useful for structured data. However, inefficient use of UNNEST can significantly increase data processing.

Example: Query Event Parameters Efficiently

SELECT event_name, ep.key, ep.value.string_valueFROM `your_project.dataset.events`,UNNEST(event_params) AS epWHERE ep.key = 'page_location';

Best practice:
Only unnest the fields you need. Avoid flattening entire arrays unless necessary.

Why it matters:
Unnecessary unnesting increases the amount of data processed, which directly increases cost.

6. Switch to Reserved Slots for Predictable Workloads

If your organization runs large and consistent query workloads, consider switching from on-demand pricing to reserved slots.

On-demand pricing: pay per query based on data processed
Reserved slots (flat-rate): fixed monthly cost for dedicated compute capacity

How to Check Query Costs

SELECT project_id, user_email,total_bytes_billed/POWER(10,9) AS GB_BilledFROM `region-us.INFORMATION_SCHEMA.JOBS_BY_PROJECT`WHERE creation_time BETWEEN TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)AND CURRENT_TIMESTAMP();

Why it matters:
If usage is predictable and high, reserved slots can significantly reduce total BigQuery costs.

7. Set Up BigQuery Cost Alerts

Unexpected BigQuery costs can be avoided by setting up billing alerts.

How to set alerts:

Navigate to Google Cloud Console → Billing → Budgets and Alerts
Set a monthly budget (for example, $500)
Enable email notifications as thresholds are reached

Why it matters:
Cost alerts provide visibility into spending and help prevent unexpected overages.

8. Automate Cost Optimization with AI

Google Cloud provides BigQuery Recommendations, an AI-powered feature that analyzes usage patterns and suggests optimizations.

How to access:

Open Google Cloud Console
Navigate to BigQuery → Recommendations
Review suggestions such as:
- Unused datasets
- Inefficient queries
- Over-provisioned resources

Why it matters:
These recommendations offer quick, actionable insights to reduce costs without manual analysis.

Final Thoughts: Spend Smarter, Not More

With the right BigQuery cost optimization strategies, you can significantly reduce spend while maintaining performance.

Focus on writing efficient queries, structuring data with partitioning and clustering, and leveraging built-in tools like caching and recommendations.

Optimizing BigQuery is not just about reducing cost. It is about building a scalable, efficient data foundation that supports long-term analytics and marketing performance.

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