We performed a comparison between Cloudera Data Science Workbench and Dataiku 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."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"The solution is quite stable."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"Data Science Studio's data science model is very useful."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The most valuable feature is the set of visual data preparation tools."
"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."
"I think it would help if Data Science Studio added some more features and improved the data model."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"The ability to have charts right from the explorer would be an improvement."
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Cloudera Data Science Workbench is ranked 18th in Data Science Platforms with 2 reviews while Dataiku is ranked 11th in Data Science Platforms. Cloudera Data Science Workbench is rated 7.0, while Dataiku 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 Dataiku writes "The model is very useful". Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Google Cloud Datalab and Alteryx, whereas Dataiku is most compared with Databricks, KNIME, Alteryx, RapidMiner and Dremio. See our Cloudera Data Science Workbench vs. Dataiku report.
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