Cloudera Data Science Workbench vs Google Cloud Datalab comparison

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Executive Summary

We performed a comparison between Cloudera Data Science Workbench and Google Cloud Datalab 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).
765,386 professionals have used our research since 2012.
Featured Review
Ismail Peer
Nilesh Gode
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"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."

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"Google Cloud Datalab is very customizable.""All of the features of this product are quite good.""The APIs are valuable.""The infrastructure is highly reliable and efficient, contributing to a positive experience.""In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

More Google Cloud Datalab Pros →

Cons
"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."

More Cloudera Data Science Workbench Cons →

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience.""The product must be made more user-friendly.""There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option.""The interface should be more user-friendly.""We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

More Google Cloud Datalab Cons →

Pricing and Cost Advice
  • "It is affordable for us because we have a limited number of users."
  • "The pricing is quite reasonable, and I would give it a rating of four out of ten."
  • "The product is cheap."
  • More Google Cloud Datalab Pricing and Cost Advice →

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    Questions from the Community
    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 »
    Top Answer:The product must be made more user-friendly. Sometimes, we have to go a roundabout way and read a lot of instruction that isn't necessary. Generally, if people use the information, they have some… more »
    Top Answer:The solution is really useful. It’s an easy way to get information. I use it as a reference for analytics, sourcing information, and research.
    Ranking
    16th
    Views
    2,372
    Comparisons
    2,085
    Reviews
    1
    Average Words per Review
    353
    Rating
    6.0
    13th
    Views
    1,837
    Comparisons
    1,686
    Reviews
    2
    Average Words per Review
    631
    Rating
    8.0
    Comparisons
    Also Known As
    CDSW
    Learn More
    Overview

    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.

    Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

    Sample Customers
    IQVIA, Rush University Medical Center, Western Union
    Information Not Available
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm30%
    Computer Software Company10%
    Healthcare Company9%
    Comms Service Provider6%
    VISITORS READING REVIEWS
    Financial Services Firm16%
    Educational Organization12%
    Computer Software Company11%
    Manufacturing Company9%
    Company Size
    VISITORS READING REVIEWS
    Small Business11%
    Midsize Enterprise11%
    Large Enterprise78%
    VISITORS READING REVIEWS
    Small Business23%
    Midsize Enterprise10%
    Large Enterprise68%
    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.
    765,386 professionals have used our research since 2012.

    Cloudera Data Science Workbench is ranked 16th in Data Science Platforms with 2 reviews while Google Cloud Datalab is ranked 13th in Data Science Platforms with 5 reviews. Cloudera Data Science Workbench is rated 7.0, while Google Cloud Datalab is rated 7.6. 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 Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Alteryx, whereas Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Domino Data Science Platform, IBM SPSS Modeler and KNIME.

    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.