Cloudera Data Science Workbench vs KNIME comparison

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Executive Summary

We performed a comparison between Cloudera Data Science Workbench and KNIME 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).
765,234 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
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."

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"This solution is easy to use and especially good at data preparation and wrapping.""It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript.""The product is open-source and therefore free to use.""KNIME is easy to learn.""Key features include: very easy-to-use visual interface; Help functions and clear explanations of the functionalities and the used algorithms; Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis.""All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function.""It has allowed us to easily implement advanced analytics into various processes.""KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data."

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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."

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"The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon.""Data visualization needs improvement.""The predefined workflows could use a bit of improvement.""KNIME's licensing and data management aren't as straightforward relative to Alteryx. Alteryx's tools are more sophisticated, so you need fewer to use it compared to KNIME. I think tab implementation could be easier, too.""It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME.""The ability to handle large amounts of data and performance in processing need to be improved.""Both RapidMiner and KNIME should be made easier to use in the field of deep learning.""They should look at other vendors like Alteryx that are more user friendly and modern."

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Pricing and Cost Advice
  • "It is free of cost. It is GNU licensed."
  • "KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required."
  • "KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website."
  • "The price of KNIME is quite reasonable and the designer tool can be used free of charge."
  • "It's an open-source solution."
  • "The price for Knime is okay."
  • "At this time, I am using the free version of Knime."
  • "This is an open-source solution that is free to use."
  • More KNIME 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: I've never had any problems with stability.
    Top Answer:We're using the free academic license just locally. I went for KNIME because they have a free academic license. And to be honest, I never bothered to check the prices.
    Top Answer:In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could… more »
    Ranking
    16th
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    Average Words per Review
    353
    Rating
    6.0
    4th
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    11,490
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    22
    Average Words per Review
    475
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    7.9
    Comparisons
    Also Known As
    CDSW
    KNIME Analytics Platform
    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.

    KNIME is an open-source analytics software used for creating data science that is built on a GUI based workflow, eliminating the need to know code. The solution has an inherent modular workflow approach that documents and stores the analysis process in the same order it was conceived and implemented, while ensuring that intermediate results are always available. 

    KNIME supports Windows, Linux, and Mac operating systems and is suitable for enterprises of all different sizes. With KNIME, you can perform functions ranging from basic I/O to data manipulations, transformations and data mining. It consolidates all the functions of the entire process into a single workflow. The solution covers all main data wrangling and machine learning techniques, and is based on visual programming.

    KNIME Features

    KNIME has many valuable key features. Some of the most useful ones include:

    • Scalability through data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
    • High extensibility via a well-defined API for plugin extensions
    • Intuitive user interface
    • Import/export of workflows
    • Parallel execution on multi-core systems
    • Command line version for "headless" batch executions
    • Activity dashboard
    • Reporting & statistics
    • Third-party integrations
    • Workflow management
    • Local automation
    • Metanode linking
    • Tool blending
    • Big Data extensions

    KNIME Benefits

    There are many benefits to implementing KNIME. Some of the biggest advantages the solution offers include:

    • Integrated Deployment: KNIME’s integrated deployment moves both the selected model, and the entire data model preparation process into production simply and automatically, allowing for continuous optimization in production and also saving time because it eliminates error.
    • Elastic and Hybrid Execution: KNIME’s elastic and hybrid executions helps you reduce costs while covering periods of high demand, dynamically.
    • Metadata Mapping: KNIME enables complete metadata mapping of all aspects of your workflow. In addition, KNIME offers blueprint workflows for documenting the nodes, data sources, and libraries used, as well as runtime information.
    • Guided Analytics: KNIME’s guided analytics applications can be customized based on reusable components.
    • Powerful analytics, local automation, and workflow difference: KNIME uses advanced predictive and machine learning algorithms to provide you with the analytics you need. In combination with powerful analytics, KNIME’s automation capabilities and workflow difference prepare your organization with the tools you need to make better business decisions.
    • Supports enterprise-wide data science practices: The deployment and management functionalities of KNIME make it easy to productionize data science applications and services, and deliver usable, reliable, and reproducible insights for the business.
    • Helps you leverage insights gained from your data: Using KNIME ensures the data science process immediately reflects changing requirements or new insights.

    Reviews from Real Users

    Below are some reviews and helpful feedback written by PeerSpot users currently using the KNIME solution.

    An Emeritus Professor at a university says, “It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional. It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.”

    Benedikt S., CEO at SMH - Schwaiger Management Holding GmbH, explains, “All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function. Technical support has been extremely responsive so far. The solution has a very strong and supportive community that shares information and helps each other troubleshoot. The solution is very stable. The initial setup is pretty simple and straightforward.”

    Piotr Ś., Test Engineer at ProData Consult, says, “What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.”

    Sample Customers
    IQVIA, Rush University Medical Center, Western Union
    Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm30%
    Computer Software Company10%
    Healthcare Company9%
    Comms Service Provider6%
    REVIEWERS
    University23%
    Comms Service Provider17%
    Retailer14%
    Government9%
    VISITORS READING REVIEWS
    Manufacturing Company12%
    Financial Services Firm11%
    Computer Software Company10%
    Educational Organization8%
    Company Size
    VISITORS READING REVIEWS
    Small Business11%
    Midsize Enterprise11%
    Large Enterprise78%
    REVIEWERS
    Small Business29%
    Midsize Enterprise27%
    Large Enterprise45%
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise14%
    Large Enterprise67%
    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.
    765,234 professionals have used our research since 2012.

    Cloudera Data Science Workbench is ranked 16th in Data Science Platforms with 2 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Cloudera Data Science Workbench is rated 7.0, while KNIME 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 KNIME writes "A low-code platform that reduces data mining time by linking script". Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Domino Data Science Platform, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Weka and Dataiku Data Science Studio.

    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.