Snowflake vs. Redshift
Cloud-Native Consumption vs. AWS Monopoly Integration
Redshift defined the cloud data warehouse era. Snowflake decoupled compute from storage to revolutionize it.
📊 Scoring Matrix
Decoupled (Multi-cluster)
Tightly coupled (Historically)
Near-zero (Fully SaaS)
Requires DBA tuning (VACUUM)
Infinite auto-scaling
Concurrency Scaling limits
Multi-cloud (AWS, GCP, Azure)
AWS ecosystem lock-in
Native data marketplace
AWS Data Exchange only
Per-second compute risk
Predictable cluster pricing
📋 Executive Summary
Snowflake is the objectively superior technology. Redshift is the financially predictable choice if you are already locked into an AWS EDP commitment.
Snowflake compute expenses can spiral uncontrollably if queries aren't optimized. Redshift offers predictable costs but requires expensive DBA salaries.
🎯 Decision Framework
- ✓ Multi-cloud strategy
- ✓ Spiky analytical workloads
- ✓ Zero DB admin capacity
- ✓ B2B data sharing requirements
- ✓ Massive AWS EDP spend commitment
- ✓ Predictable 24/7 analytical workloads
- ✓ Deep AWS ecosystem integration', 'Fixed OpEx budgets
Need a data warehouse today that 'just works' regardless of scale? Snowflake. Deep in AWS, with an enterprise discount, and strict fixed budgets? Redshift.
🌐 Market Context
Databricks is now the primary challenger to Snowflake, blurring the lines between data warehouse and data lake.
Snowflake continues incredible enterprise growth, while Redshift maintains its massive installed base by slowly adopting decoupled features.
🛠️ Related Tools
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