Snowflake vs. BigQuery vs. Redshift in 2025: A Practitioner's Take

Every month someone asks me which cloud data warehouse they should use. The honest answer is: it depends — but 'it depends' without a framework is useless advice. After running workloads on all three in production, here's how I actually make this decision for clients.

The Three Dimensions That Matter When I evaluate data warehouses for a client, I look at three things first: their existing cloud ecosystem, their query patterns (ad-hoc vs. scheduled, large scans vs. point lookups), and their team's SQL maturity. The best warehouse is the one your team will actually use well.

When to Choose Snowflake Snowflake's separation of storage and compute is genuinely differentiated. If you need to scale compute independently from storage — spinning up large clusters for month-end reporting and sizing down for daily queries — Snowflake handles this more elegantly than its competitors. It's also the most cloud-agnostic, which matters if you're running a multi-cloud strategy. The tradeoff: it's the most expensive option at scale.

When to Choose BigQuery BigQuery is the right choice if you're already committed to GCP and you have large, infrequent analytical queries. Its serverless model means you never provision clusters — you pay per byte scanned, which is economical for teams with variable query patterns. The BigQuery ML capabilities are also genuinely useful if your analysts want to run models without leaving SQL. Downside: the pricing model can surprise you if you have chatty BI tools running constantly.

When to Choose Redshift Redshift Serverless has changed the math significantly for AWS-native shops. If your data stack is already deeply AWS (Glue for ETL, S3 for storage, Lambda for triggering), the integration story with Redshift is unmatched. Redshift Spectrum for querying S3 data in place is also genuinely powerful. It's no longer the legacy choice — but it remains the right choice primarily for teams who live in AWS.