BigQuery

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:

1. Evaluate fit: Already on Google Cloud? Strong fit.
2. Estimate costs: Model query patterns and data volumes
3. Plan data loading: How will data get into BigQuery?
4. Design datasets: Tables, partitioning, clustering
5. 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.