We performed a comparison between Cloudera Data Science Workbench and Databricks based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"The Cloudera Data Science Workbench is customizable and easy to use."
"The simplicity of development is the most valuable feature."
"I like cloud scalability and data access for any type of user."
"Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily."
"The integration with Python and the notebooks really helps."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"The technical support is good."
"It is a cost-effective solution."
"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The tool's MLOps is not good. It's pricing also needs to improve."
"Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage."
"Anyone who doesn't know SQL may find the product difficult to work with."
"There is room for improvement in visualization."
"It would be very helpful if Databricks could integrate with platforms in addition to Azure."
"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."
"Databricks can improve by making the documentation better."
"A lot of people are required to manage this solution."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
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Cloudera Data Science Workbench is ranked 18th in Data Science Platforms with 2 reviews while Databricks is ranked 1st in Data Science Platforms with 78 reviews. Cloudera Data Science Workbench is rated 7.0, while Databricks is rated 8.2. The top reviewer of Cloudera Data Science Workbench writes "Useful for data science modeling but improvement is needed in MLOps and pricing ". On the other hand, the top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". Cloudera Data Science Workbench is most compared with Amazon SageMaker, Microsoft Azure Machine Learning Studio, Dataiku, Google Cloud Datalab and Alteryx, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio. See our Cloudera Data Science Workbench vs. Databricks report.
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