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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Jupyter Notebook

    Score8.6 out of 10
    N/AJupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for…N/A

    KNIME Analytics Platform

    Score7.8 out of 10
    N/AKNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.

    $0

    per month

    Pricing
    Jupyter NotebookKNIME Analytics Platform
    Editions & Modules
    No answers on this topic
    KNIME Community Hub Personal Plan
    $0
    KNIME Analytics Platform
    $0
    KNIME Community Hub Team Plan
    €99
    per month 3 users
    KNIME Business Hub
    From €35,000
    per year
    Offerings
    Pricing Offerings
    Jupyter NotebookKNIME Analytics Platform
    Free Trial
    NoNo
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details
    More Pricing Information
    Community Pulse
    Jupyter NotebookKNIME Analytics Platform
    Considered Both Products
    Open Source
    No answer on this topic
    KNIME
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    23 Answers
    94%
    Would buy again
    16 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    23 Answers
    100%
    Delivers good value for the price
    16 Answers
    Happy with the feature set
    96%
    Happy with the feature set
    22 Answers
    94%
    Happy with the feature set
    16 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    17 Answers
    83%
    Lived up to sales and marketing promises
    10 Answers
    Implementation went as expected
    95%
    Implementation went as expected
    20 Answers
    93%
    Implementation went as expected
    14 Answers
    Features
    Jupyter NotebookKNIME Analytics Platform
    Platform Connectivity
    Comparison of Platform Connectivity features of Jupyter Notebook and KNIME Analytics Platform
    Feature
    Jupyter Notebook
    9.0
    22 Ratings
    7% above category average
    KNIME Analytics Platform
    9.2
    19 Ratings
    9% above category average
    Connect to Multiple Data Sources10.022 Ratings9.619 Ratings
    Extend Existing Data Sources10.021 Ratings10.010 Ratings
    Automatic Data Format Detection8.514 Ratings9.119 Ratings
    MDM Integration7.415 Ratings7.98 Ratings
    Data Exploration
    Comparison of Data Exploration features of Jupyter Notebook and KNIME Analytics Platform
    Feature
    Jupyter Notebook
    7.0
    22 Ratings
    19% below category average
    KNIME Analytics Platform
    8.1
    18 Ratings
    4% below category average
    Visualization6.022 Ratings8.018 Ratings
    Interactive Data Analysis8.022 Ratings8.118 Ratings
    Data Preparation
    Comparison of Data Preparation features of Jupyter Notebook and KNIME Analytics Platform
    Feature
    Jupyter Notebook
    9.5
    22 Ratings
    14% above category average
    KNIME Analytics Platform
    8.3
    19 Ratings
    1% above category average
    Interactive Data Cleaning and Enrichment10.021 Ratings9.019 Ratings
    Data Transformations10.022 Ratings9.519 Ratings
    Data Encryption8.514 Ratings7.47 Ratings
    Built-in Processors9.314 Ratings7.48 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of Jupyter Notebook and KNIME Analytics Platform
    Feature
    Jupyter Notebook
    9.3
    22 Ratings
    9% above category average
    KNIME Analytics Platform
    8.0
    18 Ratings
    6% below category average
    Multiple Model Development Languages and Tools10.021 Ratings9.517 Ratings
    Automated Machine Learning9.218 Ratings8.117 Ratings
    Single platform for multiple model development10.022 Ratings9.318 Ratings
    Self-Service Model Delivery8.020 Ratings5.08 Ratings
    Model Deployment
    Comparison of Model Deployment features of Jupyter Notebook and KNIME Analytics Platform
    Feature
    Jupyter Notebook
    10.0
    20 Ratings
    16% above category average
    KNIME Analytics Platform
    7.3
    11 Ratings
    15% below category average
    Flexible Model Publishing Options10.020 Ratings8.611 Ratings
    Security, Governance, and Cost Controls10.019 Ratings5.94 Ratings
    Best Alternatives
    Jupyter NotebookKNIME Analytics Platform
    Small Businesses
    RapidMiner
    Score8.9 out of 10
    RapidMiner
    Score8.9 out of 10
    Medium-sized Companies
    Anaconda
    Score8.8 out of 10
    Anaconda
    Score8.8 out of 10
    Enterprises
    IBM Watson Studio
    Score10 out of 10
    IBM Watson Studio
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Jupyter NotebookKNIME Analytics Platform
    Likelihood to Recommend
    10.0
    (23 ratings)
    9.6
    (22 ratings)
    Likelihood to Renew
    -
    (0 ratings)
    9.5
    (4 ratings)
    Usability
    10.0
    (2 ratings)
    9.0
    (3 ratings)
    Support Rating
    9.0
    (1 ratings)
    9.3
    (6 ratings)
    Implementation Rating
    -
    (0 ratings)
    7.0
    (2 ratings)
    Ease of integration
    -
    (0 ratings)
    10.0
    (1 ratings)
    User Testimonials
    Jupyter NotebookKNIME Analytics Platform
    Likelihood to Recommend
    Open Source
    I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.
    Incentivized
    Read full review
    KNIME
    KNIME Analytics Platform is excellent for people who are finding Excel frustrating, this can be due to errors creeping in due to manual changes or simply that there are too many calculations which causes the system to slow down and crash. This is especially true for regular reporting where a KNIME Analytics Platform workflow can pull in the most recent data, process it and provide the necessary output in one click. I find KNIME Analytics Platform especially useful when talking with audiences who are intimidated by code. KNIME Analytics Platform allows us to discuss exactly how data is processed and an analysis takes place at an abstracted level where non-technical users are happy to think and communicate which is often essential when they are subject matter experts whom you need for guidance. For experienced programmers KNIME Analytics Platform is a double-edged sword. Often programmers wish to write their own code because they are more efficient working that way and are constrained by having to think and implement work in nodes. However, those constraints forcing development in a "KNIME way" are useful when working in teams and for maintenance compared to some programmers' idiosyncratic styles.
    Incentivized
    Read full review
    Pros
    Open Source
    • Simple and elegant code writing ability. Easier to understand the code that way.
    • The ability to see the output after each step.
    • The ability to use ton of library functions in Python.
    • Easy-user friendly interface.
    Incentivized
    Read full review
    KNIME
    • Summarize instrument level financial data with relevant statistics
    • Map transactions from core extracts to groups of like transactions using rule engines
    • Machine learning using random forests and other techniques to analyze data and identify correlations for use in predictive models
    • Fill out sampling data from averages.
    Read full review
    Cons
    Open Source
    • Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
    • Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
    Incentivized
    Read full review
    KNIME
    • It does not have proper visualization.
    • Some other BI tools (QlikView) have much easier functions for data interaction.
    • Some other BI tools (Tableau) can be set up much faster.
    • It is not an easy tool to use for non-tech savvy staff.
    Incentivized
    Read full review
    Likelihood to Renew
    Open Source
    No answers on this topic
    KNIME
    We are happy with Knime product and their support. Knime AP is versatile product and even can execute Python scripts if needed. It also supports R execution as well; however, it is not being used at our end
    Read full review
    Usability
    Open Source
    Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.
    Incentivized
    Read full review
    KNIME
    KNIME Analytics Platform offers a great tradeoff between intuitiveness and simplicity of the user interface and almost limitless flexibility. There are tools that are even easier to adopt by someone new to analytics, but none that would provide the scalability of KNIME when the user skills and application complexity grows
    Incentivized
    Read full review
    Support Rating
    Open Source
    I haven't had a need to contact support. However, all required help is out there in public forums.
    Incentivized
    Read full review
    KNIME
    KNIME's HQ is in Europe, which makes it hard for US companies to get customer service in time and on time. Their customer service also takes on average 1 to 2 weeks to follow up with your request. KNIME's documentation is also helpful but it does not provide you all the answers you need some of the time.
    Incentivized
    Read full review
    Implementation Rating
    Open Source
    No answers on this topic
    KNIME
    KNIME Analytics Platform is easy to install on any Windows, Mac or Linux machine. The KNIME Server product that is currently being replaced by the KNIME Business Hub comes as multiple layers of software and it took us some time to set up the system right for stability. This was made harder by KNIME staff's deeper expertise in setting up the Server in Linux rather than Windows environment. The KNIME Business Hub promises to have a simpler architecture, although currently there is no visibility of a Windows version of the product.
    Incentivized
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    Alternatives Considered
    Open Source
    With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.
    Incentivized
    Read full review
    KNIME
    Having used both the Alteryx and [KNIME Analytics] I can definitely feel the ease of using the software of Alteryx. The [KNIME Analytics] on the other hand isn't that great but is 90% of what Alteryx can do along with how much ease it can do. Having said that, the 90% functionality and UI at no cost would be enough for me to quit using Alteryx and move towards [KNIME Analytics].
    Incentivized
    Read full review
    Return on Investment
    Open Source
    • Positive impact: flexible implementation on any OS, for many common software languages
    • Positive impact: straightforward duplication for adaptation of workflows for other projects
    • Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration
    Incentivized
    Read full review
    KNIME
    • It is suited for data mining or machine learning work but If we're looking for advanced stat methods such as mixed effects linear/logistics models, that needs to be run through an R node.
    • Thinking of our peers with an advanced visualization techniques requirement, it is a lagging product.
    Incentivized
    Read full review
    ScreenShots

    KNIME Analytics Platform Screenshots

    Screenshot of the KNIME Modern UI. This is the the new user interface for the KNIME Analytics Platform that is available with improved look and feel as the default interface, from KNIME Analytics Platform version 5.1.0 release.Screenshot of the KNIME Analytics Platform user interface - the KNIME Workbench - displays the current, open workflow(s). Here is the general user interface layout — application tabs, side panel, workflow editor and node monitor.Screenshot of the KNIME user interface elements — workflow toolbar, node action bar, rename components and metanodes.Screenshot of the entry page, which is displayed by clicking the Home tab. From here users can; check out example workflows to get started, access a local workspace, or even start a new workflow by clicking the yellow plus button.Screenshot of the status of a KNIME node, which shows whether it's configured, not configured, executed, or has an error.Screenshot of the KNIME node action bar, which can be used to configure, execute, cancel, reset, and - when available - open the view.