What are the main differences between Snowflake and Amazon Redshift for a growing data warehouse?

All answers to this question.

The biggest differentiator is the architecture of scaling. Snowflake was built from the ground up to separate compute from storage, meaning you can scale your "Virtual Warehouses" up or down instantly without moving data. Redshift has made strides here with RA3 nodes, but it still requires more "knob-turning" from a DBA. If you don't have a large team, Snowflake’s "near-zero management" is a lifesaver—it handles things like micro-partitioning and query optimization automatically. However, Redshift can be more cost-effective if you have a very predictable, steady-state workload and are already heavily invested in the AWS ecosystem.

   Answered 2025-05-16 by Emily Thompson

Have you looked into the specific concurrency requirements of your BI tools, or are you more concerned about the raw speed of your overnight ETL batch processing?

   Answered 2025-05-18 by Michael Richardson

• Michael, our main pain point is actually concurrency. On Monday mornings, when all our department heads run their Tableau dashboards at once, our current system crawls to a halt. Based on what Emily said, Snowflake’s ability to spin up additional clusters automatically to handle those peak hours sounds like exactly what we need. I’m tired of getting calls at 8 AM about spinning wheels on dashboards, so a platform that scales out horizontally without manual intervention is a high priority for us.

     Commented 2025-05-20 by David Miller

Snowflake's "Time Travel" feature is also worth mentioning. Being able to query data as it existed at any point in the last 90 days is a huge safety net for accidental deletes.

   Answered 2025-05-22 by Jessica Taylor

• I agree with Jessica. As someone who has accidentally dropped a production table before, that Time Travel feature is worth every penny of the storage cost for the peace of mind.

     Commented 2025-05-04 by Sarah Jenkins