Setting up BigQuery Analytics

Overview

WALT Carbon’s BigQuery Analytics integration provides advanced data analysis capabilities, custom reporting, and deep insights into your GCP usage patterns. This comprehensive setup guide ensures you get maximum value from your analytics investment.

This is essential for enterprise usage - BigQuery analytics unlock advanced cost optimization and usage insights critical for large-scale GCP management.

Prerequisites

Before setting up BigQuery analytics:

Admin role in WALT Carbon platform
BigQuery Admin permissions in your GCP project
Billing account access for BigQuery usage
Basic understanding of SQL queries (recommended)

Step 1: Enable BigQuery APIs

Ensure BigQuery APIs are enabled in your GCP project:

# Enable required APIs
gcloud services enable bigquery.googleapis.com
gcloud services enable bigquerydatatransfer.googleapis.com
gcloud services enable bigqueryreservation.googleapis.com

# Verify APIs are enabled
gcloud services list --enabled --filter="bigquery"

WALT Carbon can automatically enable these APIs during setup if you grant the necessary permissions.

Step 2: Configure Service Account Permissions

Grant WALT Carbon’s service account BigQuery permissions:

Using Console
Using CLI

Go to IAM & Admin > IAM in Google Cloud Console
Find the WALT Carbon service account
Add these roles:
- BigQuery Admin
- BigQuery Data Editor
- BigQuery Job User
- BigQuery Resource Viewer

# Replace with your service account and project
SA_EMAIL="[email protected]"
PROJECT_ID="your-project-id"

# Grant BigQuery permissions
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA_EMAIL" \
    --role="roles/bigquery.admin"

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA_EMAIL" \
    --role="roles/bigquery.dataEditor"

Step 3: Access Analytics Configuration

In your WALT Carbon platform:

Navigate to Analytics > BigQuery Setup
Click Configure BigQuery Integration
Select your GCP project and dataset location

Step 4: Dataset Configuration

Create Analytics Dataset

Dataset Creation

Choose a dataset name and location:

Name: walt_carbon_analytics (recommended)
Location: Same region as your primary GCP resources
Expiration: None (data retained indefinitely)

Access Control

Configure dataset permissions:

WALT Carbon service account: Editor
Analytics team: Viewer
Finance team: Viewer (cost data only)

Data Retention

Set up data lifecycle policies:

Raw data: 12 months
Aggregated data: 3 years
Summary reports: Indefinite

Data Export Configuration

Configure which data to export to BigQuery:

Cost and Billing Data

Daily Exports

Detailed cost breakdowns by service
Project-level spending analysis
Resource utilization metrics
Budget vs. actual comparisons

Schema Preview

-- Example cost data table structure
CREATE TABLE cost_data (
  date DATE,
  project_id STRING,
  service_name STRING,
  sku_description STRING,
  cost_amount FLOAT64,
  currency STRING,
  usage_amount FLOAT64,
  usage_unit STRING
);

Security and Compliance Data

Security Findings

Vulnerability scan results
Compliance status by resource
Risk assessments and scores
Remediation tracking

Schema Preview

-- Example security data table structure
CREATE TABLE security_findings (
  scan_date TIMESTAMP,
  resource_name STRING,
  finding_type STRING,
  severity STRING,
  status STRING,
  remediation_date TIMESTAMP
);

Resource and Usage Data

Resource Inventory

Compute instances and specifications
Storage usage and performance
Network traffic patterns
Service configurations

Performance Metrics

CPU and memory utilization
Storage I/O patterns
Network bandwidth usage
Application performance indicators

Step 5: Set Up Data Pipelines

Automated Data Refresh

Configure how frequently data is updated:

Real-time (Streaming)
Batch (Scheduled)
On-demand

Best for: Live dashboards, alerting Frequency: Continuous updates Cost: Highest Use cases:

Real-time cost monitoring
Security incident detection
Performance anomaly alerts

Data Transformation

Set up data processing pipelines:

Data Cleaning
- Remove test/development data
- Standardize naming conventions
- Handle missing values
Data Enrichment
- Add business context (cost centers, teams)
- Calculate derived metrics
- Apply business rules and mappings
Data Aggregation
- Create summary tables for faster queries
- Pre-calculate common metrics
- Build time-series aggregations

Step 6: Create Custom Dashboards

Pre-built Dashboard Templates

WALT Carbon provides several dashboard templates:

Executive Dashboard

High-level cost and security metrics for leadership

Financial Operations

Detailed cost analysis and budget tracking

Security Operations

Security posture and compliance monitoring

Resource Optimization

Usage patterns and optimization opportunities

Custom Dashboard Creation

Define Requirements

Identify key stakeholders and their needs
List required metrics and KPIs
Determine refresh frequency and data sources

Design Visualizations

Choose appropriate chart types
Set up filters and drill-down capabilities
Configure alerting thresholds

Build and Test

Create dashboard in your preferred BI tool
Test with sample data
Validate calculations and aggregations

Deploy and Share

Set up user access and permissions
Schedule automated reports
Provide training to end users

Step 7: Advanced Analytics Features

Machine Learning Integration

Leverage BigQuery ML for predictive analytics:

-- Example: Cost forecasting model
CREATE OR REPLACE MODEL `walt_carbon_analytics.cost_forecast_model`
OPTIONS(
  model_type='ARIMA_PLUS',
  time_series_timestamp_col='date',
  time_series_data_col='daily_cost'
) AS
SELECT
  date,
  SUM(cost_amount) as daily_cost
FROM `walt_carbon_analytics.cost_data`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 12 MONTH)
GROUP BY date
ORDER BY date;

Custom Analytics Queries

Common analytical queries:

Cost Analysis Queries

-- Top 10 most expensive services this month
SELECT 
  service_name,
  SUM(cost_amount) as total_cost
FROM `walt_carbon_analytics.cost_data`
WHERE date >= DATE_TRUNC(CURRENT_DATE(), MONTH)
GROUP BY service_name
ORDER BY total_cost DESC
LIMIT 10;

-- Cost trend analysis
SELECT 
  DATE_TRUNC(date, MONTH) as month,
  SUM(cost_amount) as monthly_cost,
  LAG(SUM(cost_amount)) OVER (ORDER BY DATE_TRUNC(date, MONTH)) as prev_month_cost
FROM `walt_carbon_analytics.cost_data`
GROUP BY month
ORDER BY month;

Security Analysis Queries

-- Security findings by severity
SELECT 
  severity,
  COUNT(*) as finding_count,
  COUNT(CASE WHEN status = 'RESOLVED' THEN 1 END) as resolved_count
FROM `walt_carbon_analytics.security_findings`
WHERE scan_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY severity;

-- Mean time to resolution
SELECT 
  AVG(DATE_DIFF(remediation_date, scan_date, DAY)) as avg_resolution_days
FROM `walt_carbon_analytics.security_findings`
WHERE status = 'RESOLVED'
  AND remediation_date IS NOT NULL;

Step 8: Cost Optimization

Managing BigQuery Costs

Cost Optimization Strategies

Query Optimization
- Use column selection instead of SELECT *
- Implement partitioning and clustering
- Cache frequently used query results
Storage Management
- Set up data lifecycle policies
- Use compressed storage formats
- Archive old data to cheaper storage classes
Usage Monitoring
- Set up query cost alerts
- Monitor slot usage and concurrency
- Use reservation pricing for predictable workloads

BigQuery Pricing Considerations

On-Demand Pricing
Flat-Rate Pricing

Best for: Variable workloads, getting started

Pay per TB processed
No upfront commitment
Automatic scaling

Monitoring and Maintenance

Performance Monitoring

Track key metrics:

Query performance and execution times
Data freshness and pipeline health
User adoption and dashboard usage
Cost per query and total spending

Regular Maintenance Tasks

Weekly

Review query performance
Check data pipeline health
Monitor storage growth

Monthly

Analyze usage patterns
Optimize expensive queries
Review and update data retention policies

Quarterly

Assess business value and ROI
Plan new analytics use cases
Review and optimize costs

Troubleshooting

Common Issues

Data Export Failures

Symptoms: Data not appearing in BigQuerySolutions:

Check service account permissions
Verify API quotas and limits
Review export job logs
Ensure dataset location matches source data

Slow Query Performance

Symptoms: Dashboards loading slowlySolutions:

Implement table partitioning
Add appropriate clustering
Use materialized views for complex aggregations
Optimize query structure and filters

Unexpected Costs

Symptoms: Higher than expected BigQuery billsSolutions:

Review query patterns and optimize
Implement query result caching
Set up cost controls and alerts
Consider flat-rate pricing for high usage

Integration with BI Tools

Connect BigQuery to popular business intelligence platforms:

Google Data Studio

Native integration with no additional setup required

Tableau

Use BigQuery connector for real-time dashboards

Power BI

Connect via BigQuery connector in Power BI Desktop

Looker

Seamless integration with Google Cloud’s native BI tool

Next Steps

After completing BigQuery analytics setup:

Enable Two-Factor Authentication for enhanced security
Configure Cost Optimization Rules based on analytics insights
Set up Advanced Alerting using BigQuery-powered notifications
Train Your Team on using the new analytics capabilities

Support

Need help with BigQuery analytics?

📧 Analytics support: [email protected]
📚 BigQuery documentation: Available in platform help center
🎓 Training sessions: Schedule through your account manager
💬 Community forum: Share queries and best practices

Monitor your BigQuery usage closely during the first month to understand cost patterns and optimize accordingly. Set up billing alerts to prevent unexpected charges.

Getting Started

Core Features

Essential Guides

Advanced Features

​Overview

​Prerequisites

​Step 1: Enable BigQuery APIs

​Step 2: Configure Service Account Permissions

​Step 3: Access Analytics Configuration

​Step 4: Dataset Configuration

​Create Analytics Dataset

​Data Export Configuration

​Step 5: Set Up Data Pipelines

​Automated Data Refresh

​Data Transformation

​Step 6: Create Custom Dashboards

​Pre-built Dashboard Templates

Executive Dashboard

Financial Operations

Security Operations

Resource Optimization

​Custom Dashboard Creation

​Step 7: Advanced Analytics Features

​Machine Learning Integration

​Custom Analytics Queries

​Step 8: Cost Optimization

​Managing BigQuery Costs

​BigQuery Pricing Considerations

​Monitoring and Maintenance

​Performance Monitoring

​Regular Maintenance Tasks

​Troubleshooting

​Common Issues

​Integration with BI Tools

Google Data Studio

Tableau

Power BI

Looker

​Next Steps

​Support

Overview

Prerequisites

Step 1: Enable BigQuery APIs

Step 2: Configure Service Account Permissions

Step 3: Access Analytics Configuration

Step 4: Dataset Configuration

Create Analytics Dataset

Data Export Configuration

Step 5: Set Up Data Pipelines

Automated Data Refresh

Data Transformation

Step 6: Create Custom Dashboards

Pre-built Dashboard Templates

Custom Dashboard Creation

Step 7: Advanced Analytics Features

Machine Learning Integration

Custom Analytics Queries

Step 8: Cost Optimization

Managing BigQuery Costs

BigQuery Pricing Considerations

Monitoring and Maintenance

Performance Monitoring

Regular Maintenance Tasks

Troubleshooting

Common Issues

Integration with BI Tools

Next Steps

Support