Amazon SageMaker vs Denodo

• Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
• Customization and easy to alter and change.
• If you already are an Amazon user, you do not need to transition over to another software.

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• Flat learning curve.
• Does not need brilliant developers.
• Less need for custom denodo wrappers, even if needed is not very complex.
• Denodo stored procedures and functions helped address complex, not straightforward requirements.
• Export/import deployment between environments was very helpful.

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• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

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• Lack of Documentation and user community: Denodo continues to have a small community of super users, therefore there is not a lot of documentation out there in the event you get stuck.
• Cost: Data virtualization isn't cheap, so smaller organizations might shy away from this technology due to the high licensing costs.
• Caching strategy: Some slower datasets will require caching... which argues, why not just do traditional ETL if you need to do this?

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• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

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• Improved time-to-market. We addressed new requirements in less than 2 person days.
• Very agile. Easy to handle changing requirements. Like a new requirement, a change in requirement was not expensive either.
• Platform issues were difficult to diagnose and debug.

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