"The Cloudera Data Science Workbench is customizable and easy to use."
"It's great technology."
"Ability to work collaboratively without having to worry about the infrastructure."
"The technical support is good."
"The solution is easy to use and has a quick start-up time due to being on the cloud."
"The integration with Python and the notebooks really helps."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"The initial setup is pretty easy."
"I haven't heard about any major stability issues. At this time I feel like it's stable."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"I have seen better user interfaces, so that is something that can be improved."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"There are no direct connectors — they are very limited."
"Would be helpful to have additional licensing options."
"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."
"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."
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Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis.
Databricks creates a Unified Analytics Platform that accelerates innovation by unifying data science, engineering, and business. It utilizes Apache Spark to help clients with cloud-based big data processing. It puts Spark on “autopilot” to significantly reduce operational complexity and management cost. The Databricks I/O module (DBIO) improves the read and write performance of Apache Spark in the cloud. An increase in productivity is ensured through Databricks’ collaborative workplace.
Cloudera Data Science Workbench is ranked 15th in Data Science Platforms with 1 review while Databricks is ranked 2nd in Data Science Platforms with 23 reviews. Cloudera Data Science Workbench is rated 8.0, while Databricks is rated 7.8. The top reviewer of Cloudera Data Science Workbench writes "Customizable, easy to install, and easy to use". On the other hand, the top reviewer of Databricks writes "Has a good feature set but it needs samples and templates to help invite users to see results". Cloudera Data Science Workbench is most compared with Dataiku Data Science Studio, Amazon SageMaker, Anaconda, Microsoft Azure Machine Learning Studio and Alteryx, whereas Databricks is most compared with Microsoft Azure Machine Learning Studio, Amazon SageMaker, Azure Stream Analytics, Alteryx and Apache Flink.
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