We performed a comparison between Anaconda and Cloudera Data Science Workbench 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 most valuable feature is the set of libraries that are used to support the functionality that we require."
"The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors."
"The notebook feature is an improvement over RStudio."
"The most advantageous feature is the logic building."
"I can use Anaconda for non-heavy tasks."
"The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code."
"With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages."
"The virtual environment is very good."
"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."
"A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."
"The solution would benefit from offering more automation."
"The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform."
"The interface could be improved. Other solutions, like Visual Studio, have much better UI."
"It also takes up a lot of space."
"One thing that hurts the product is that the company is not doing more to advertise it as a solution and make it more well known."
"One feature that I would like to see is being able to use a different language in a different cell, which would allow me to mix R and Python together."
"Anaconda should be optimized for RAM consumption."
"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."
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Anaconda is ranked 13th in Data Science Platforms with 17 reviews while Cloudera Data Science Workbench is ranked 19th in Data Science Platforms with 2 reviews. Anaconda is rated 8.0, while Cloudera Data Science Workbench is rated 7.0. The top reviewer of Anaconda writes "Offers free version and is helpful to handle small-scale workloads". On the other hand, the top reviewer of Cloudera Data Science Workbench writes "Useful for data science modeling but improvement is needed in MLOps and pricing ". Anaconda is most compared with Databricks, Microsoft Azure Machine Learning Studio, Amazon SageMaker, Microsoft Power BI and IBM SPSS Statistics, whereas Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Dataiku and Google Cloud Datalab. See our Anaconda vs. Cloudera Data Science Workbench report.
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We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
