Snowflake vs Redshift

Snowflake and Amazon Redshift are major cloud data warehouses. Redshift was AWS's pioneering MPP warehouse; Snowflake built a cloud-native architecture that decouples storage and compute from the ground up.

Redshift was the warehouse that proved cloud analytics could be fast and affordable; Snowflake arrived later with an architecture purpose-built for the cloud's elasticity. AWS has since modernized Redshift considerably, so the comparison today is closer than it was a few years ago.

Key Differences

Portability is the first split. Snowflake runs on AWS, Azure, and GCP. Redshift is AWS-only, which is a strength inside AWS and a constraint outside it.

Architecture is the second. Snowflake separated storage and compute from day one, so you scale compute independently and pay only for what runs. Redshift historically coupled them in node-based clusters, though RA3 nodes (with managed storage) and Redshift Serverless now offer decoupling and reduce operational overhead. Snowflake still leads on hands-off operations, requiring little tuning, while Redshift has traditionally rewarded careful distribution-key and sort-key design, less so with Serverless.

AWS integration is where Redshift pulls ahead. It ties natively into S3, Glue, IAM, and Redshift Spectrum for querying data in the lake, making it a smooth fit for AWS-centric platforms. Snowflake integrates with AWS too but as an external service.

Both handle concurrency well, Snowflake via multi-cluster warehouses and Redshift via concurrency scaling. Pricing models differ but neither is clearly cheaper; it depends on workload and optimization.

Workload isolation is a recurring theme. Snowflake's multiple independent virtual warehouses make it trivial to keep heavy ETL from slowing interactive dashboards, each runs on separate compute over the same data. Redshift achieves similar isolation with concurrency scaling and, increasingly, with Redshift Serverless, but the model grew out of a shared-cluster heritage. Tuning expectations differ too: classic Redshift rewarded careful choice of distribution and sort keys, while Snowflake aims to need almost none of that, and Redshift Serverless moves in the same hands-off direction.

When to Choose Snowflake

Choose Snowflake for multi-cloud or non-AWS environments, when you want minimal tuning, or when cross-organization data sharing matters. Its clean separation of compute and storage and low operational burden appeal to teams that want analytics without warehouse administration.

When to Choose Redshift

Choose Redshift when your data platform is AWS-centric and you value native integration with S3, Glue, IAM, and Spectrum. Existing Redshift investments, skills, and tight AWS coupling make it the pragmatic choice within that ecosystem, especially with Serverless lowering the operational bar.

Integration depth is Redshift's trump card inside AWS. Querying S3 directly with Spectrum, cataloging with Glue, governing with Lake Formation and IAM, and feeding from Kinesis all feel native. Snowflake integrates with these too, but as a third party. If your data gravity and security model already live in AWS, that native cohesion carries real weight.

Verdict

Snowflake leads on portability and hands-off operation; Redshift leads on native AWS integration and cost alignment within AWS. If you are all-in on AWS, Redshift (especially Serverless or RA3) is compelling. If you want multi-cloud flexibility and minimal tuning, Snowflake is the stronger fit.