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."The Cloudera Data Science Workbench is customizable and easy to use."
"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 built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"It's great technology."
"The integration with Python and the notebooks really helps."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."
"The time travel feature is the solution's most valuable aspect."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."
"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."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"Costs can quickly add up if you don't plan for it."
"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."
"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."
"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."
"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."
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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, Microsoft Azure Machine Learning Studio and Dremio. See our Cloudera Data Science Workbench vs. Databricks report.
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