Snowflake vs BigQuery

Data warehouses.

Overview

Snowflake and BigQuery are the two leading cloud data warehouses, and they optimise for different things. Snowflake is cloud-agnostic (AWS, GCP, Azure), separates compute from storage, and bills per warehouse-second. BigQuery is GCP-native, serverless on the compute side, and bills per byte scanned. The right answer depends on which cloud the org runs on and whether the team prefers warehouse-sized provisioning (Snowflake) or query-cost-driven (BigQuery).

• Snowflake: cloud-agnostic plus separated compute. Runs on AWS, GCP, or Azure. Compute warehouses scale independently of storage; pay per warehouse-second.
• BigQuery: GCP-native plus serverless. No compute provisioning; pay per byte scanned. Tight integration with GCS, Pub/Sub, Dataflow, Looker.
• Pricing model differs. Snowflake's warehouse-second billing rewards consistent workloads; BigQuery's per-scan billing rewards selective queries and well-partitioned tables.
• Operational fit per team. Existing cloud and BI tooling biases the choice. Document the rationale per team rather than re-deriving it per project.

The approach

Workload-driven choice, per-team operational fit considered, documented rationale. The discipline is making the warehouse choice once with a written reason and aligning analytics tooling to that choice rather than running both warehouses in parallel.

• Workload-driven. Choice per workload. Reality drives the answer rather than tribal preference.
• Snowflake for cloud-agnostic plus consistent workloads. Multi-cloud orgs and teams with steady warehouse utilisation.
• BigQuery for GCP-native plus selective queries. GCP-heavy stacks with bursty, well-partitioned analytics workloads.
• Operational fit plus documented rationale. Team workflow considered; per-warehouse rationale captured. Future migrations have a paper trail.

Why this compounds

The right warehouse choice compounds across years. Wrong choices pay query-cost or operational-fit penalties indefinitely; right choices pay neither. By year two the team's analytics tooling and BI semantics are aligned with the warehouse and migration costs become real.

• Better operational fit. Warehouse matches team and cloud. Velocity stays high.
• Workload-driven decisions. Replaces tribal preference with documented rationale. Quality of choice improves.
• Better engineering velocity. Right warehouse matches the team's existing tooling. BI integrations work without exotic adapters.
• Year-one investment, year-two habit. First warehouse choice is the investment; subsequent projects inherit the patterns.