Anaconda vs Cloudera Data Science Workbench comparison

Cancel
You must select at least 2 products to compare!
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Anaconda and Cloudera Data Science Workbench based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms.
To learn more, read our detailed Data Science Platforms Report (Updated: March 2024).
763,955 professionals have used our research since 2012.
Featured Review
Maruf-Hossain
Ismail Peer
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code.""The solution is stable.""The notebook feature is an improvement over RStudio.""It helped us find find the optimal area for where our warehouse should be located.""The documentation is excellent and the solution has a very large and active community that supports it.""The most advantageous feature is the logic building.""It's interesting. It's user friendly. That's what makes it outstanding among the others. It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science.""The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results."

More Anaconda Pros →

"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."

More Cloudera Data Science Workbench Pros →

Cons
"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.""The interface could be improved. Other solutions, like Visual Studio, have much better UI.""When you install Anaconda for the first time, it's really difficult to update it.""I think that the framework can be improved to make it easier for people to discover and use things on their own.""I think better documentation or a step-by-step guide for installation would help, especially for on-premise users.""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.""The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform.""Anaconda should be optimized for RAM consumption."

More Anaconda Cons →

"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."

More Cloudera Data Science Workbench Cons →

Pricing and Cost Advice
  • "The licensing costs for Anaconda are reasonable."
  • "The product is open-source and free to use."
  • "My company uses the free version of the tool. There is also a paid version of the tool available."
  • More Anaconda Pricing and Cost Advice →

    report
    Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
    763,955 professionals have used our research since 2012.
    Questions from the Community
    Top Answer:Pricing is a matter of open source versus proprietary. Anaconda is open source and openly publishes their pricing models. RapidMiner is proprietary and you must receive a quote depending on your use… more »
    Top Answer: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… more »
    Top Answer:The tool's MLOps is not good. It's pricing also needs to improve.
    Top Answer:We have different use cases. Our banking use case uses machine learning to identify customer life events and recommend the best-suited card products. These machine-learning models are deployed in our… more »
    Ranking
    18th
    Views
    2,965
    Comparisons
    2,207
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    16th
    Views
    2,372
    Comparisons
    2,085
    Reviews
    1
    Average Words per Review
    353
    Rating
    6.0
    Comparisons
    Also Known As
    CDSW
    Learn More
    Overview

    Anaconda makes it easy for you to install and maintain Python environments. Our development team tests to ensure compatibility of Python packages in Anaconda. We support and provide open source assurance for packages in Anaconda to mitigate your risk in using open source and meet your regulatory compliance requirements.

    Python is the fastest growing language for data science. Anaconda includes 720+ Python open source packages and now includes essential R packages. This powerful combination allows you to do everything you want from BI to advanced modeling on complex Big Data

    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.

    Sample Customers
    LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone
    IQVIA, Rush University Medical Center, Western Union
    Top Industries
    REVIEWERS
    Financial Services Firm27%
    Manufacturing Company18%
    Non Tech Company9%
    Energy/Utilities Company9%
    VISITORS READING REVIEWS
    Financial Services Firm17%
    Computer Software Company11%
    Government10%
    Manufacturing Company6%
    VISITORS READING REVIEWS
    Financial Services Firm30%
    Computer Software Company10%
    Healthcare Company9%
    Manufacturing Company6%
    Company Size
    REVIEWERS
    Small Business41%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise13%
    Large Enterprise69%
    VISITORS READING REVIEWS
    Small Business12%
    Midsize Enterprise11%
    Large Enterprise77%
    Buyer's Guide
    Data Science Platforms
    March 2024
    Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms. Updated: March 2024.
    763,955 professionals have used our research since 2012.

    Anaconda is ranked 18th in Data Science Platforms with 1 review while Cloudera Data Science Workbench is ranked 16th in Data Science Platforms with 1 review. Anaconda is rated 7.8, 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 Data Science Studio and Google Cloud Datalab.

    See our list of best Data Science Platforms vendors.

    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.