Redshift

Redshift is a reasonable choice for organizations deep in the AWS ecosystem with steady, predictable analytical workloads. The RA3 node types and Redshift Serverless have addressed some of the original architecture's rigidity. If your team manages AWS infrastructure comfortably, your data volumes are stable, and your query patterns are predictable, Redshift can deliver solid performance without a migration.

Redshift also integrates tightly with the rest of the AWS data stack: S3 for staging, Glue for cataloging, SageMaker for ML, and IAM for access control. If your security and compliance model is built around AWS, staying on Redshift avoids the cross-cloud complexity that a migration would introduce.

The clearest signal is cost growth that outpaces data growth. Redshift's provisioned-cluster model bills for reserved capacity, which suits steady utilization. Teams with variable workloads, seasonal patterns, or growing idle periods often find that consumption-based pricing on Snowflake or serverless options on MotherDuck match their spend more closely, and Redshift Serverless brings the same elasticity within AWS.

The second signal is how the tuning surface maps to your team. Redshift gives you fine-grained control through distribution keys, sort keys, vacuum schedules, and workload management queues, and that control rewards teams who want to manage it deliberately. If your team would rather spend its time on analytics than on warehouse configuration, a more hands-off model may be the better match.

We also see Redshift clusters whose sizing was set for an earlier peak workload and is worth revisiting as needs change. A coffee chain migrated from Redshift to MotherDuck, eliminating cluster management entirely and shifting to a pricing model that matched their query volume.

We do not default to migration. We start with an audit of your cluster configuration, query patterns, and cost structure. If tuning (distribution keys, sort keys, compression, WLM configuration) can deliver the performance and cost profile you need, we implement those changes. Many teams have never revisited these settings since initial setup, and the improvements can be dramatic.

When migration is the right call, dbt makes it faster. Because dbt separates transformation logic from warehouse-specific syntax, a well-structured dbt project can move between Redshift, Snowflake, BigQuery, and MotherDuck with modest changes. We run both environments in parallel, validate that every downstream report produces the same results, and cut over only after confirmation.