We performed a comparison between Cloudera Data Science Workbench and Databricks based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."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 solution is an impressive tool for data migration and integration."
"The processing capacity is tremendous in the database."
"The setup was straightforward."
"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."
"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."
"It's very simple to use Databricks Apache Spark."
"Easy to use and requires minimal coding and customizations."
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"The tool's MLOps is not good. It's pricing also needs to improve."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"Anyone who doesn't know SQL may find the product difficult to work with."
"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."
"Pricing is one of the things that could be improved."
"In the next release, I would like to see more optimization features."
"I would like more integration with SQL for using data in different workspaces."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"Costs can quickly add up if you don't plan for it."
"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."
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Cloudera Data Science Workbench is ranked 17th 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 Data Science Studio, Google Cloud Datalab and Alteryx, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio.
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