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BigQuery Cleanup Guide: Optimize Storage, Reduce Costs, and Improve Data Governance
Regular BigQuery cleanup is essential for cost savings, query performance, and data governance. Learn best practices for managing datasets, optimizing storage, and securing access.
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.
Keeping your BigQuery environment clean isn’t just a housekeeping task, it’s a vital practice for organizations managing large-scale data warehouses. Without regular maintenance, unused datasets, inefficient queries, and excessive user access can drive up costs and degrade performance. In this guide, we’ll explore BigQuery cleanup best practices, from storage optimization to data governance, and how they can transform your analytics operations.
What is involved in BQ Cleanup?
BigQuery cleanup refers to the systematic process of auditing and optimizing your Google BigQuery environment. It focuses on removing obsolete datasets, streamlining storage, improving query efficiency, and strengthening security. The ultimate goal? A leaner, faster, and more cost-effective data warehouse.
Here’s what’s typically included in a thorough cleanup:
Remove Unused Datasets and Tables
Unused datasets and tables often accumulate as projects evolve or teams change. These inactive assets not only clutter your environment but also incur ongoing storage costs.
Use INFORMATION_SCHEMA.TABLES to identify tables with no recent read or write activity.
Set lifecycle policies to automatically delete stale data after a defined period.
Archive historical data to Cloud Storage if retention is required for compliance.
Audit Temporary and Test Tables
Temporary tables created during ad hoc analysis often linger long after they’re needed. These “digital breadcrumbs” can pile up, increasing both complexity and cost.
Regularly scan for tables with names like
temp_,test_, or date stamps.Automate cleanup with scheduled jobs that flag and remove these tables after 30–90 days of inactivity.
3. Optimize Oversized Tables
Large tables, especially those with years of historical data or unused columns, can degrade query performance and inflate storage bills.
Implement partitioning (by date or ID ranges) to minimize query scans.
Apply clustering on frequently filtered columns to reduce data read.
Consider column pruning to drop unused fields.
4. Review Scheduled Queries and Materialized Views
It’s not uncommon for scheduled queries and materialized views to keep running long after the dashboards they power have been deprecated.
Audit INFORMATION_SCHEMA.JOBS_BY_PROJECT for recurring jobs and assess their relevance.
Disable or delete jobs referencing outdated datasets.
Consolidate redundant views to simplify maintenance.
5. Strengthen Data Governance With Permission Audits
Over time, user and service account permissions can drift, leading to excessive access and potential security risks.
Review IAM policies to ensure the principle of least privilege.
Revoke access for former employees or deprecated service accounts.
Monitor INFORMATION_SCHEMA.OBJECT_PRIVILEGES for unexpected grants.
6. Analyze Query Performance for Cost Efficiency
Inefficient queries, like missing filters, cross joins, or SELECT * patterns, can significantly drive up processing costs.
Use the Query Execution Plan to identify bottlenecks.
Optimize joins and filters to minimize processed data.
Replace complex subqueries with materialized views where appropriate.
Why Regular BigQuery Cleanup Matters
A consistent cleanup routine delivers benefits that go far beyond aesthetics:
✅ Reduce Costs – BigQuery charges for both active storage and query processing. Trimming unused datasets and improving query logic directly lowers your monthly bills.
✅ Boost Performance – Smaller, optimized datasets mean faster queries and reduced time-to-insight for your analytics teams.
✅ Enhance Security and Compliance – Removing unnecessary access and maintaining clear data structures strengthens your organization’s data governance posture.
How Often Should You Perform BigQuery Cleanup?
For most organizations, a quarterly cleanup is sufficient to maintain a healthy environment. However, highly dynamic environments or enterprises with frequent deployments may benefit from monthly cleanups, particularly for monitoring temporary tables and scheduled jobs.
To stay proactive, consider setting up automated cleanup reports in BigQuery:
Example automated report fields:
Top 10 largest inactive tables
Orphaned scheduled queries
Redundant materialized views
Permissions granted to deprecated service accounts
This visibility allows your team to address issues before they impact costs or performance.
Keep BigQuery Clean and Lean With Napkyn
At Napkyn, we help organizations like yours keep BigQuery environments optimized and secure. From one-time cleanups to ongoing monitoring and automation, our experts ensure your data warehouse is ready for growth.
Whether you’re struggling with runaway storage costs, slow queries, or unclear user permissions, Napkyn can help you build a sustainable, high-performance BigQuery strategy.
👉 Schedule a consultation today to learn how we can help you unlock the full potential of BigQuery.
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BigQuery Cleanup Guide: Optimize Storage, Reduce Costs, and Improve Data Governance
Regular BigQuery cleanup is essential for cost savings, query performance, and data governance. Learn best practices for managing datasets, optimizing storage, and securing access.
Shreya Banker
Data Scientist
July 16, 2025
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.
Keeping your BigQuery environment clean isn’t just a housekeeping task, it’s a vital practice for organizations managing large-scale data warehouses. Without regular maintenance, unused datasets, inefficient queries, and excessive user access can drive up costs and degrade performance. In this guide, we’ll explore BigQuery cleanup best practices, from storage optimization to data governance, and how they can transform your analytics operations.
What is involved in BQ Cleanup?
BigQuery cleanup refers to the systematic process of auditing and optimizing your Google BigQuery environment. It focuses on removing obsolete datasets, streamlining storage, improving query efficiency, and strengthening security. The ultimate goal? A leaner, faster, and more cost-effective data warehouse.
Here’s what’s typically included in a thorough cleanup:
Remove Unused Datasets and Tables
Unused datasets and tables often accumulate as projects evolve or teams change. These inactive assets not only clutter your environment but also incur ongoing storage costs.
Use INFORMATION_SCHEMA.TABLES to identify tables with no recent read or write activity.
Set lifecycle policies to automatically delete stale data after a defined period.
Archive historical data to Cloud Storage if retention is required for compliance.
Audit Temporary and Test Tables
Temporary tables created during ad hoc analysis often linger long after they’re needed. These “digital breadcrumbs” can pile up, increasing both complexity and cost.
Regularly scan for tables with names like
temp_,test_, or date stamps.Automate cleanup with scheduled jobs that flag and remove these tables after 30–90 days of inactivity.
3. Optimize Oversized Tables
Large tables, especially those with years of historical data or unused columns, can degrade query performance and inflate storage bills.
Implement partitioning (by date or ID ranges) to minimize query scans.
Apply clustering on frequently filtered columns to reduce data read.
Consider column pruning to drop unused fields.
4. Review Scheduled Queries and Materialized Views
It’s not uncommon for scheduled queries and materialized views to keep running long after the dashboards they power have been deprecated.
Audit INFORMATION_SCHEMA.JOBS_BY_PROJECT for recurring jobs and assess their relevance.
Disable or delete jobs referencing outdated datasets.
Consolidate redundant views to simplify maintenance.
5. Strengthen Data Governance With Permission Audits
Over time, user and service account permissions can drift, leading to excessive access and potential security risks.
Review IAM policies to ensure the principle of least privilege.
Revoke access for former employees or deprecated service accounts.
Monitor INFORMATION_SCHEMA.OBJECT_PRIVILEGES for unexpected grants.
6. Analyze Query Performance for Cost Efficiency
Inefficient queries, like missing filters, cross joins, or SELECT * patterns, can significantly drive up processing costs.
Use the Query Execution Plan to identify bottlenecks.
Optimize joins and filters to minimize processed data.
Replace complex subqueries with materialized views where appropriate.
Why Regular BigQuery Cleanup Matters
A consistent cleanup routine delivers benefits that go far beyond aesthetics:
✅ Reduce Costs – BigQuery charges for both active storage and query processing. Trimming unused datasets and improving query logic directly lowers your monthly bills.
✅ Boost Performance – Smaller, optimized datasets mean faster queries and reduced time-to-insight for your analytics teams.
✅ Enhance Security and Compliance – Removing unnecessary access and maintaining clear data structures strengthens your organization’s data governance posture.
How Often Should You Perform BigQuery Cleanup?
For most organizations, a quarterly cleanup is sufficient to maintain a healthy environment. However, highly dynamic environments or enterprises with frequent deployments may benefit from monthly cleanups, particularly for monitoring temporary tables and scheduled jobs.
To stay proactive, consider setting up automated cleanup reports in BigQuery:
Example automated report fields:
Top 10 largest inactive tables
Orphaned scheduled queries
Redundant materialized views
Permissions granted to deprecated service accounts
This visibility allows your team to address issues before they impact costs or performance.
Keep BigQuery Clean and Lean With Napkyn
At Napkyn, we help organizations like yours keep BigQuery environments optimized and secure. From one-time cleanups to ongoing monitoring and automation, our experts ensure your data warehouse is ready for growth.
Whether you’re struggling with runaway storage costs, slow queries, or unclear user permissions, Napkyn can help you build a sustainable, high-performance BigQuery strategy.
👉 Schedule a consultation today to learn how we can help you unlock the full potential of BigQuery.
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BigQuery Cleanup Guide: Optimize Storage, Reduce Costs, and Improve Data Governance
Shreya Banker
Data Scientist
Jul 16, 2025
Read More
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Ricardo Cristofolini
Senior Implementation Specialist, Data Solutions
Jul 4, 2025
Read More
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Shreya Banker
Data Scientist
Jul 2, 2025
Read More
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