Analyzing Cloud Storage Usage and Cost with BigQuery

As organizations store growing volumes of data in the cloud, understanding storage usage and cost becomes critical for cost optimization and capacity planning. Google BigQuery provides powerful tools to analyze cloud storage metrics efficiently using SQL and large-scale analytics.

1. Why Analyze Cloud Storage Usage and Cost?

Cloud storage costs can increase quickly due to:

Unused or duplicate data

Long-term storage that is no longer needed

Inefficient data formats or partitioning

Analyzing usage and cost helps you:

Control and reduce expenses

Identify waste and inefficiencies

Forecast future storage needs

Improve data lifecycle management

2. Understanding BigQuery Storage Costs

BigQuery charges for storage based on:

Active storage (data modified in the last 90 days)

Long-term storage (data not modified for 90+ days)

Logical storage (uncompressed data size)

Physical storage (compressed data size)

Costs vary depending on storage class and region.

3. Data Sources for Analysis

To analyze storage usage and cost, BigQuery provides system views such as:

INFORMATION_SCHEMA.TABLE_STORAGE

INFORMATION_SCHEMA.TABLES

Cloud billing export tables (via BigQuery)

These datasets allow visibility into table size, storage type, and cost drivers.

4. Analyzing Storage Usage with BigQuery

a. Storage Usage by Dataset

You can analyze how much storage each dataset consumes:

Identify large datasets

Track growth over time

Prioritize optimization efforts

Key metrics include:

Total logical bytes

Total physical bytes

Long-term vs active storage

b. Storage Usage by Table

At the table level, you can:

Detect oversized or unused tables

Identify tables with frequent updates (higher cost)

Evaluate partitioned vs non-partitioned tables

This helps determine where cleanup or restructuring is needed.

5. Cost Analysis Using Billing Export Data

By exporting Google Cloud Billing data to BigQuery, you can:

Break down storage costs by project, dataset, or table

Track monthly and daily spending

Identify cost spikes and anomalies

This allows cost attribution to teams or applications.

6. Identifying Optimization Opportunities

a. Move Data to Long-Term Storage

Data that is rarely modified automatically becomes cheaper after 90 days. Avoid unnecessary updates that reset the storage timer.

b. Delete or Archive Unused Data

Use access logs and query history to find:

Tables that are rarely queried

Old snapshots or backups

Archive them to cheaper storage or delete them.

c. Optimize Table Design

Use partitioning to reduce scanned data

Use clustering to improve query efficiency

Store data in efficient formats

Well-designed tables reduce both storage and query costs.

7. Monitoring Storage Trends

By scheduling queries and dashboards, you can:

Monitor storage growth over time

Set alerts for abnormal increases

Forecast future costs

This supports proactive cost management.

8. Security and Governance Considerations

When analyzing usage and cost:

Apply least-privilege access to billing data

Ensure compliance with data retention policies

Track storage usage by project or department

This improves accountability and governance.

9. Best Practices

Regularly review storage usage reports

Automate cleanup of temporary or staging tables

Separate raw, processed, and archived data

Combine cost analysis with query usage analysis

Conclusion

BigQuery provides a powerful and scalable way to analyze cloud storage usage and cost. By leveraging system metadata and billing data, organizations can gain deep visibility into how data is stored, used, and billed—enabling smarter decisions, lower costs, and better data management.

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