Databricks Lakehouse Platform vs Denodo

• It supports all data science programming languages like Python and R
• it takes very few minutes to deploy models into production
• it has tools that ensures collaborations between developers

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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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• Better Localized Testing
• When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
• Graphing Support went non-existent; when it was one of their compelling general engine.

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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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• Rapid growth of analytics within our company.
• Cost model aligns with usage allowing us to make a reasonable initial investment and scale the cost as we realize the value.
• Platform is easy to learn and Databricks provides excellent support and training.
• Platform does not require a large DevOPs investment

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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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