What Is BigQuery?
BigQuery is Google Cloud’s enterprise data warehouse, designed for large-scale data analytics. It’s fully managed and serverless, you don’t provision infrastructure or manage servers. Just load data and query it using standard SQL.
Key characteristics:
- Serverless architecture
- Petabyte-scale analytics
- Standard SQL support
- Built-in ML (BigQuery ML)
- Real-time analytics capable
- Pay-per-query or flat-rate pricing
BigQuery Architecture
Serverless Model
- No infrastructure to manage
- Automatic scaling
- Always available
- No capacity planning
Separation of Storage and Compute
- Data stored in Capacitor format
- Compute dynamically allocated
- Independent scaling
Columnar Storage
- Optimized for analytical queries
- Efficient compression
- Fast aggregations
BigQuery for Finance
Common financial use cases:
Financial reporting
- Large-scale data consolidation
- Historical analysis across years
- Complex calculations and aggregations
FP&A analytics
- Budget vs. actual analysis
- Forecasting with ML
- Scenario modeling
Customer analytics
- Revenue by customer segment
- Lifetime value calculations
- Churn analysis
Operational finance
- Transaction analysis
- Fraud detection
- Spend analytics
BigQuery vs. Snowflake
| Aspect | BigQuery | Snowflake |
|---|---|---|
| Cloud | Google only | AWS, Azure, GCP |
| Architecture | Serverless | Virtual warehouses |
| Pricing | Per-query or flat | Per-second compute |
| Streaming | Built-in | Via Snowpipe |
| ML | BigQuery ML | Snowpark ML |
| Sharing | Analytics Hub | Data Marketplace |
| Management | Zero | Near-zero |
Both are excellent choices; selection often depends on existing cloud investments.
BigQuery Pricing
Storage:
- Active storage: Per TB/month
- Long-term storage: Reduced rate (90+ days)
Compute (Analysis):
- On-demand: Per TB scanned
- Flat-rate: Reserved capacity
Cost optimization tips:
- Use partitioning and clustering
- Query only needed columns
- Use cached results
- Set cost controls
BigQuery Features
Standard SQL
- Full SQL support
- User-defined functions
- Scripting and procedures
BigQuery ML
- Train ML models with SQL
- No data movement required
- Integration with Vertex AI
Streaming Ingestion
- Real-time data loading
- Sub-second availability
- Event-driven analytics
Data Sharing
- Analytics Hub
- Authorized views
- Cross-project access
Integrations
- Google services (GA4, Ads)
- Looker (native)
- Third-party connectors
How Go Fig Works with BigQuery
Go Fig integrates with BigQuery for financial analytics:
BigQuery as source:
- Query BigQuery datasets
- Use existing tables and views
- Leverage BigQuery ML models
BigQuery as destination:
- Load data from ERPs, CRMs
- Build unified analytics layer
- Enable broad data access
Go Fig value-add:
- Semantic layer on BigQuery
- Excel delivery without SQL
- Combine with non-Google sources
- AI insights with Celeste
BigQuery Ecosystem
Data loading:
- Cloud Storage transfers
- Dataflow (streaming/batch)
- Fivetran, Airbyte
- BigQuery Data Transfer Service
Transformation:
- dbt
- Dataform (Google)
- Scheduled queries
Visualization:
- Looker (native integration)
- Looker Studio (free)
- Third-party BI tools
Getting Started with BigQuery
For finance teams considering BigQuery:
- Evaluate fit: Already on Google Cloud? Strong fit.
- Estimate costs: Model query patterns and data volumes
- Plan data loading: How will data get into BigQuery?
- Design datasets: Tables, partitioning, clustering
- Choose access layer: Looker, Go Fig, or SQL direct
Go Fig provides a business-friendly layer, making BigQuery data accessible to finance teams through Excel and dashboards without requiring SQL expertise.