Dataiku vs KNIME comparison

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Dataiku Logo
9,109 views|7,135 comparisons
100% willing to recommend
Knime Logo
10,966 views|7,554 comparisons
93% willing to recommend
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Dataiku and KNIME 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.
To learn more, read our detailed Dataiku vs. KNIME Report (Updated: May 2024).
771,740 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
"Data Science Studio's data science model is very useful.""Cloud-based process run helps in not keeping the systems on while processes are running.""The solution is quite stable.""Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors.""I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person.""The most valuable feature is the set of visual data preparation tools.""The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction.""If many teams are collaborating and sharing Jupyter notebooks, it's very useful."

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"It is a stable solution...It is a scalable solution.""The most useful features are the readily available extensions that speed up the work.""The most valuable is the ability to seamlessly connect operators without the need for extensive programming.""It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea.""I was able to apply basic algorithms through just dragging and dropping.""The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way.""Clear view of the data at every step of ETL process enables changing the flow as needed.""This open-source product can compete with category leaders in ELT software."

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Cons
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable.""Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days).""I think it would help if Data Science Studio added some more features and improved the data model.""Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku.""The ability to have charts right from the explorer would be an improvement.""The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective.""In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin.""I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

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"Though I can use KNIME in a 64-bit platform in the lab, it's missing some features. For example, from my laptop, I can use the image reader feature of KNIME. However, in the lab, the image reader node is missing.""System resource usage. Knime will occupy total system RAM size and other applications will hang.""​The data visualization part is the area most in need of improvement.""It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge.""Compared to the other data tools on the market, the user interface can be improved.""It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end.""They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning.""They should look at other vendors like Alteryx that are more user friendly and modern."

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Pricing and Cost Advice
  • "The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."
  • "Pricing is pretty steep. Dataiku is also not that cheap."
  • More Dataiku 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:Databricks and Dataiku are excellent Data Science platforms but have different strengths and weaknesses. Below is a comparison of the two products based on several parameters Cost It is… more »
    Top Answer:Hi, I am the founder of Actable AI so my answer may be biased. In terms of performance, it's Actable AI. Why? Because we leverage the best and latest open source technologies out there (AutoGluon… more »
    Top Answer:Dataiku is my choice as it's not bulky and the learning path for people like me (noobs in ML and data science) is not steep at all, so after a couple of pieces of training I feel very confident. Also… more »
    Top Answer:Since KNIME is a no-code platform, it is easy to work with.
    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:KNIME is not good at visualization. I would like to see NLQ (Natural language query) and automated visualizations added to KNIME.
    Ranking
    11th
    Views
    9,109
    Comparisons
    7,135
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    4th
    Views
    10,966
    Comparisons
    7,554
    Reviews
    21
    Average Words per Review
    501
    Rating
    7.9
    Comparisons
    Databricks logo
    Compared 35% of the time.
    Alteryx logo
    Compared 13% of the time.
    RapidMiner logo
    Compared 9% of the time.
    Amazon SageMaker logo
    Compared 5% of the time.
    RapidMiner logo
    Compared 26% of the time.
    Microsoft Power BI logo
    Compared 20% of the time.
    Alteryx logo
    Compared 13% of the time.
    Weka logo
    Compared 8% of the time.
    Also Known As
    Dataiku DSS
    KNIME Analytics Platform
    Learn More
    Overview

    Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

    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
    BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
    Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm18%
    Educational Organization14%
    Manufacturing Company8%
    Computer Software Company8%
    REVIEWERS
    University25%
    Comms Service Provider17%
    Retailer14%
    Government8%
    VISITORS READING REVIEWS
    Manufacturing Company12%
    Financial Services Firm11%
    Computer Software Company9%
    Educational Organization8%
    Company Size
    REVIEWERS
    Small Business57%
    Large Enterprise43%
    VISITORS READING REVIEWS
    Small Business12%
    Midsize Enterprise19%
    Large Enterprise68%
    REVIEWERS
    Small Business28%
    Midsize Enterprise26%
    Large Enterprise46%
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise14%
    Large Enterprise67%
    Buyer's Guide
    Dataiku vs. KNIME
    May 2024
    Find out what your peers are saying about Dataiku vs. KNIME and other solutions. Updated: May 2024.
    771,740 professionals have used our research since 2012.

    Dataiku is ranked 11th in Data Science Platforms with 7 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Dataiku is rated 8.2, while KNIME is rated 8.2. The top reviewer of Dataiku writes "The model is very useful". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". Dataiku is most compared with Databricks, Alteryx, RapidMiner, Microsoft Azure Machine Learning Studio and Amazon SageMaker, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Weka and Microsoft Azure Machine Learning Studio. See our Dataiku vs. KNIME report.

    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.