Amazon Redshift vs Databricks SQL: which is better for mixed BI + ML?

Databricks SQL is the stronger fit for teams that mix BI dashboards with ML model training, because its lakehouse keeps one copy of data queryable by both SQL and Spark or Python jobs. Redshift handles BI well on its own, but its machine learning support runs through Redshift ML, which issues SQL CREATE MODEL statements that hand training off to Amazon SageMaker Autopilot instead of running inside the warehouse.

How do Redshift and Databricks SQL differ on architecture and ML tooling?

When does Redshift fit a mixed BI and ML team?

Redshift works well when the ML side of the work stays close to SQL: classification or regression models trained through Redshift ML and queried with plain SELECT statements next to BI dashboards. Teams already running their reporting stack on AWS avoid standing up a second platform, and Redshift Spectrum lets the same cluster query data sitting in S3 without loading it first.

When does Databricks SQL fit better?

Databricks SQL fits better once ML work goes beyond built-in prediction, into feature engineering or custom Python and Spark pipelines that need to read the same tables the BI team queries. MLflow, which Databricks open-sourced in 2018, tracks experiments and model versions in the same workspace as the SQL warehouse, so data scientists and analysts work from one copy of the data instead of exporting between systems.

Which one should a mixed BI+ML team choose?

Analytics teams whose ML needs stop at built-in SQL prediction, and who are already committed to AWS, get less operational overhead from Redshift. Teams running custom model training or a full ML lifecycle alongside BI reporting get more from Databricks SQL's shared lakehouse, even outside the AWS-native stack.