BigQuery
What it is: Google Cloud's serverless data warehouse. Analyze petabytes with SQL. Zero infrastructure management.
What It Does Best
Massive scale queries. Scan terabytes in seconds. Automatic scaling handles any workload size.
Serverless simplicity. No cluster management, no capacity planning. Just query your data.
Standard SQL. Use familiar SQL syntax. No proprietary query language to learn.
Key Features
Serverless: No servers to manage or provision
Separation of compute and storage: Scale independently
ML built-in: Create ML models with SQL (BigQuery ML)
Streaming inserts: Real-time data ingestion
Federated queries: Query external data sources
Pricing
On-demand: $5/TB scanned (first 1TB/month free)
Flat-rate: $2,000+/month for dedicated capacity
Storage: $0.02/GB/month (active), $0.01/GB (long-term)
Streaming: $0.05/GB ingested
When to Use It
✅ Large-scale analytics (terabytes to petabytes)
✅ Ad-hoc analysis without infrastructure setup
✅ Already using Google Cloud Platform
✅ Need fast query performance at scale
✅ Want serverless data warehouse
When NOT to Use It
❌ Small datasets (under 100GB) - cheaper options exist
❌ Need low-latency transactional queries (use Cloud SQL)
❌ Real-time updates critical (batch-oriented)
❌ Not on GCP and don't want vendor lock-in
❌ Budget-sensitive (costs can escalate with full table scans)
Common Use Cases
Data warehouse: Central analytics repository for business data
Log analysis: Query application logs and metrics at scale
ML pipelines: Train models directly on warehouse data
Business intelligence: Power BI tools (Looker, Tableau, etc.)
ETL destination: Load data from various sources for analysis
BigQuery vs Alternatives
vs Snowflake: BigQuery better for GCP integration, Snowflake more cloud-agnostic
vs Redshift: BigQuery fully serverless, Redshift requires cluster management
vs Athena: BigQuery faster and more features, Athena cheaper for sporadic queries
Unique Strengths
True serverless: Completely hands-off infrastructure
BigQuery ML: Build ML models with just SQL
Live streaming: Query data as it's being ingested
GCP integration: Native integration with entire Google ecosystem
Bottom line: Best serverless data warehouse for massive-scale analytics. Perfect if you're on GCP or need zero infrastructure management. Costs can add up with full table scans - use partitioning and clustering. Game-changing for teams that need petabyte-scale analysis without hiring DBAs.