How to Analyze Billing Data in Looker Studio

Looker Studio (formerly Google Data Studio) allows you to create powerful visual dashboards and reports from your Google Cloud billing data. By connecting Looker Studio to your BigQuery billing export, you can build custom cost analysis dashboards that help you understand spending patterns, identify cost optimization opportunities, and communicate cloud costs to stakeholders.

This guide shows you how to create a comprehensive billing dashboard in Looker Studio using your exported Google Cloud billing data.

Prerequisites

Before you begin, ensure you have:

• Billing export enabled to BigQuery (Enable billing export guide)
• Billing data populated in your BigQuery dataset (this can take 24-48 hours after enabling)
• Access permissions:
  • BigQuery Data Viewer role on the billing export dataset
  • Billing Account Viewer role (to verify billing account details)
• Looker Studio access (available free at lookerstudio.google.com)

Understanding Your Billing Data in BigQuery

Your billing export creates tables in BigQuery with names like:

• gcp_billing_export_v1_XXXXXX (Standard usage cost data)
• gcp_billing_export_resource_v1_XXXXXX (Detailed resource-level data)

The detailed export provides granular resource-level information, making it ideal for comprehensive cost analysis.

Step 1: Access Looker Studio

1. Open Looker Studio

   • Go to lookerstudio.google.com
   • Sign in with your Google Cloud account

2. Create a New Report

   • Click Create > Report
   • Or click Blank Report to start from scratch

Step 2: Connect to Your BigQuery Billing Data

1. Add a Data Source

   • In the new report, you'll be prompted to add data
   • Click Create New Data Source

2. Select BigQuery Connector

   • Search for and select BigQuery from the connector list
   • Click Authorize if prompted to grant Looker Studio access to BigQuery

3. Choose Your Billing Export Table

   • My Projects tab: Select the project containing your billing export dataset
   • Project: Choose your FinOps or billing administration project
   • Dataset: Select your billing export dataset (e.g., cloud_billing_data)
   • Table: Choose gcp_billing_export_resource_v1_<BILLING_ACCOUNT_ID> for detailed data

4. Configure the Data Source

   • Click Connect in the upper-right corner
   • Looker Studio will analyze the schema and display available fields

5. Add to Report

   • Click Add to Report
   • The data source is now connected and ready for visualization

Step 3: Create Key Visualizations

Visualization 1: Total Cost by Month

Purpose: Track overall spending trends over time

1. Add a Time Series Chart

   • Click Add a Chart > Time Series
   • Drag to place on your canvas

2. Configure the Chart

   • Date Dimension: usage_start_time (set to Month)
   • Metric: cost (Sum)
   • Optional: Add credits as a second metric to show net costs

3. Format the Chart

   • Chart title: "Monthly Cloud Spending Trend"
   • Add axis labels and currency formatting

Visualization 2: Cost by Service

Purpose: Identify which Google Cloud services drive the most cost

1. Add a Pie Chart or Bar Chart

   • Click Add a Chart > Pie Chart or Bar Chart

2. Configure the Chart

   • Dimension: service.description
   • Metric: cost (Sum)
   • Sort: By cost descending

3. Customize

   • Title: "Cost by Service"
   • Show top 10 services
   • Use color coding for clarity

Visualization 3: Cost by Project

Purpose: Understand which projects or teams are spending the most

1. Add a Table

   • Click Add a Chart > Table

2. Configure the Table

   • Dimensions: project.name, project.id
   • Metrics: cost (Sum), usage_amount (if relevant)
   • Sort: By cost descending

3. Add Conditional Formatting

   • Highlight high-cost projects in red
   • Use data bars to visualize relative costs

Visualization 4: Cost by SKU (Service Resource)

Purpose: Drill down into specific resource costs within services

1. Add a Bar Chart

   • Click Add a Chart > Bar Chart

2. Configure the Chart

   • Dimension: sku.description
   • Breakdown Dimension: service.description
   • Metric: cost (Sum)
   • Filter: Limit to top 20 SKUs

Visualization 5: Cost by Labels

Purpose: Track costs by team, environment, or cost center (if using labels)

1. Add a Stacked Bar Chart

   • Click Add a Chart > Stacked Bar Chart

2. Configure the Chart

   • Dimension: labels.value (for specific label key like "team" or "environment")
   • Breakdown: service.description
   • Metric: cost (Sum)

📌 Note: This requires consistent labeling practices across your Google Cloud resources.

Step 4: Add Interactive Filters

Filters allow users to explore data dynamically.

1. Add a Date Range Control

   • Click Add a Control > Date Range Control
   • Place at the top of your dashboard
   • Configure to default to "Last 30 days" or "This month"

2. Add Drop-down Filters

   • Project Filter: Click Add a Control > Drop-down List

     • Dimension: project.name
     • Allow multiple selections

   • Service Filter: Add another drop-down

     • Dimension: service.description

   • Location Filter: Add drop-down for regional analysis

     • Dimension: location.region

3. Position Filters

   • Place filters at the top of the dashboard for easy access
   • Group related filters together

Step 5: Add Calculated Fields (Optional)

Calculated fields help derive insights not directly available in the data.

Example: Net Cost (Cost minus Credits)

1. Create Calculated Field

   • In the data source, click Add a Field
   • Name: Net Cost
   • Formula: cost - credits.amount

2. Use in Visualizations

   • Replace cost metric with Net Cost in your charts

Example: Cost per Unit (if usage data available)

• Formula: cost / usage_amount
• Useful for understanding unit economics

Step 6: Use Pre-built Templates (Recommended)

Google provides official Looker Studio templates for billing analysis:

1. Access the Official Template

   • Go to the Google Cloud Billing Dashboard template
   • Click Use Template

2. Configure Data Source

   • Replace the sample data source with your billing export BigQuery table
   • Authorize access when prompted

3. Customize

   • Modify charts, colors, and layouts to match your needs
   • Add or remove visualizations based on your analysis requirements

The official template includes:

• Cost trends over time
• Cost breakdown by service and project
• Top resources by cost
• Budget vs. actual spending comparisons

Step 7: Share and Schedule Reports

1. Share the Dashboard

   • Click Share in the upper-right corner
   • Add viewer or editor emails
   • Set permissions (View or Edit)

2. Schedule Email Delivery

   • Click File > Schedule email delivery
   • Configure frequency (daily, weekly, monthly)
   • Add recipients
   • Choose PDF or link format

3. Embed in Websites (Optional)

   • Click File > Embed report
   • Copy the embed code for internal portals or wikis

Best Practices for Looker Studio Billing Dashboards

✅ Use multi-region datasets - Ensures detailed usage cost data is available

✅ Create separate dashboards for different audiences:

• Executive dashboard: High-level trends and totals
• Engineering dashboard: Service-level and SKU-level details
• Finance dashboard: Budget tracking and forecasting

✅ Refresh data regularly - Billing data updates daily; set expectations accordingly

✅ Leverage BigQuery views - Create views for common queries to improve dashboard performance

✅ Document calculations - Add descriptions to calculated fields for transparency

✅ Set up alerts - Use Looker Studio alerts or Google Cloud budgets to notify on cost spikes

Troubleshooting

Dashboard shows no data

• Verify billing export is enabled and data is flowing to BigQuery
• Check permissions: Ensure you have BigQuery Data Viewer role
• Wait for data: Billing data can take 24-48 hours to appear after enabling export

Data is incomplete or outdated

• Billing exports update daily, not in real-time
• Intraday data may be partial; rely on previous day's data for accuracy
• Check the export_time field in your BigQuery table to verify data freshness

Performance is slow

• Use aggregated tables or views: Pre-aggregate data by month/project/service
• Limit date ranges: Default to last 30 or 90 days
• Use data extracts: For very large datasets, create BigQuery extracts

Next Steps

• Set up cost allocation labels: Learn about Google Cloud labeling
• Create budget alerts: Configure billing budgets
• Explore BigQuery queries: Example billing queries
• Enable GKE cost allocation: Track Kubernetes costs