Google BigQuery is a fully managed, serverless data warehouse that enables super-fast SQL analytics over petabytes of data. Its pay-per-query pricing and zero infrastructure management make it popular with startups and enterprises alike, especially those in the Google Cloud ecosystem.
Product Overview
BigQuery's serverless architecture means there is no infrastructure to provision or manage — teams write SQL and BigQuery scales automatically. Its tight integration with Looker, Google Analytics 4, Google Ads, and Firebase makes it the natural analytics hub for Google-centric GTM teams. BigQuery ML allows teams to build and run ML models directly in SQL.
Key Features
- Serverless SQL Analytics: Run complex queries over terabytes of data in seconds with no infrastructure management.
- BigQuery ML: Build and run ML models using SQL — no separate ML infrastructure needed.
- BigQuery Omni: Query data across AWS S3 and Azure Blob without moving it into GCP.
- Streaming Inserts: Ingest and query real-time data streams with millisecond latency.
- Google Analytics Integration: Native GA4 and Google Ads data export directly into BigQuery.
Best For
Organisations in the Google Cloud ecosystem — especially those using GA4, Google Ads, or Looker — that want a scalable, serverless data warehouse.
Pricing
Pay-per-query ($5/TB scanned) or flat-rate slots. Storage at $0.02/GB/month. Significant free tier available.
Key Integrations
Looker, Tableau, dbt, Fivetran, Airbyte, Google Analytics 4, Google Ads
Pros
- Zero infrastructure to manage
- Extremely fast at scale
- Native Google ecosystem integration
- Transparent pricing
Cons
- Expensive for repeated scans of large tables without optimisation
- Less mature than Snowflake for data sharing
- Google Cloud lock-in
