BigQuery vs Snowflake vs Databricks

BigQuery charges for query processing (on-demand: $6.25/TB scanned) and storage (active: $0.02/GB/month, long-term: $0.01/GB/month). There is no charge for idle compute — you pay only when queries run. Alternatively, BigQuery Editions (Flex, Standard, Enterprise) offer slot-based committed capacity. The serverless model is excellent for sporadic workloads but can become expensive if queries scan full tables without proper partitioning and clustering. Best practice: always partition by date and cluster by high-cardinality filter columns to reduce scanned bytes by 80–99%.

Snowflake separates compute from storage. Virtual warehouses are clusters of compute nodes that you start and stop on demand. You pay per second of warehouse runtime (minimum 60 seconds). An X-Small warehouse costs ~$2/credit, and larger sizes double the cost and speed. Storage is $23/TB/month flat. The key advantage: multiple teams can query the same data simultaneously using separate warehouses with zero contention. The risk: warehouses left running idle burn credits. Snowflake Serverless Tasks and Snowpark Container Services add additional billing dimensions. Auto-suspend (15–60 seconds) is essential for cost control.

Choose Databricks when you need to blend SQL analytics with machine learning and data engineering in a single platform. Databricks Unity Catalog provides unified governance across structured, semi-structured, and unstructured data. Delta Lake (open format) avoids vendor lock-in. Databricks SQL warehouses compete directly with BigQuery and Snowflake for BI workloads, while Databricks Machine Learning handles feature engineering, model training, and MLflow tracking. The tradeoff is higher platform complexity and a steeper learning curve. Best for: companies building lakehouse architectures, teams that already use Apache Spark, or organizations with heavy ML workloads alongside analytics.

Yes, many large organizations use multiple platforms. A common pattern: BigQuery as the primary analytics warehouse (cheap storage, fast ad-hoc queries), Snowflake for secure data sharing with partners (Snowflake's native data sharing is industry-leading), and Databricks for ML pipelines that write results back to BigQuery or Snowflake. The cost of maintaining multiple platforms includes data egress fees, tooling complexity, and engineering overhead. For most companies, picking one primary warehouse and using it for 90% of workloads is more cost-effective than multi-platform sprawl.