Fazlur Rahman
Choosing a cloud data warehouse is a multi-year commitment. Snowflake, BigQuery, and Redshift each have a distinct architecture, pricing model, and ecosystem. This comparison gives you the decision framework a real team would use in 2026.
Architecture
- Snowflake — virtual warehouses on shared storage + compute separation.
- BigQuery — serverless, separates compute from storage natively.
- Redshift — provisioned clusters (RA3 separates compute from managed storage).
Pricing model
- Snowflake — per-second compute + storage. Auto-suspend aggressively.
- BigQuery — pay per query (bytes scanned) or flat-rate slots.
- Redshift — node-based, RI discounts, Serverless option.
Performance
- Snowflake — multi-cluster warehouses for high concurrency.
- BigQuery — slots determine query throughput; BI Engine for low-latency.
- Redshift — sortkey + distkey tuning; concurrency scaling for spikes.
Which to pick
- Multi-cloud + shared storage — Snowflake.
- Google ecosystem, ML-heavy — BigQuery.
- AWS-only, deep Redshift Spectrum — Redshift.
Storage and data formats
All three decouple storage from compute; their internal formats and external options differ. Snowflake stores data in micro-partitions optimised for columnar scans. BigQuery uses Capacitor, a columnar format tightly coupled to its execution engine. Redshift uses a traditional columnar format (AZ64 compression plus sortkey/distkey tuning). Each can read Parquet, ORC, Avro, JSON, and CSV from object storage via external tables or federated queries.
