You're making a lot of assertions I am not sure I agree with: > Data lakes are better for ML / AI workloads, cheaper, more flexible, and separate compute from

You're making a lot of assertions I am not sure I agree with: > Data lakes are better for ML / AI workloads, cheaper, more flexible, and separate compute from storage. With a data warehouse, you need to share compute with other users. With data lakes you can attach an arbitrary number of computational clusters to the data. - I am not sure it's any cheaper than BQ or Snowflake storage. - Modern CDW separates compute from storage. - I am not sure what you mean by "you need to share compute with others". Why? - You can attach an arbitrary number of "clusters" in BQ and Snowflake as well. Additionally, modern CDW provides a very high level of abstraction and a very high level of manageability. Their time travel and compaction actually work, and their storage systems are continuously optimized for optimal performance.