Cancel
You must select at least 2 products to compare!
SparkCognition Logo
507 views|260 comparisons
Knime Logo
11,490 views|7,931 comparisons
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Darwin 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).
765,386 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
"In terms of streamlining a lot of the low-level data science work, it does a few things there.""I find it quite simple to use. Once you are trained on the model, you can use it anyway you want.""The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types.""Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision.""The thing that I find most valuable is the ability to clean the data.""I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable.""The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate.""The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."

More Darwin Pros →

"We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics.""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.""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.""Easy to connect with every database: We use queries from SQL, Redshift, Oracle.""Stability is excellent. I would give it a nine out of ten.""The most valuable is the ability to seamlessly connect operators without the need for extensive programming.""The most valuable feature is the data wrangling, which is what I mainly use it for."

More KNIME Pros →

Cons
"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin.""There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets.""The analyze function takes a lot of time.""There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do.""The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition.""An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data.""Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model.""The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin."

More Darwin Cons →

"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.""One area that could be improved is increasing awareness and adoption of KNIME among organizations. Despite its capabilities, it is not as well-known as other tools. The advertising and marketing efforts to reach out to companies and universities have not been very successful.""The pricing needs improvement.""It could be easier to use.""Not just for KNIME, but generally for software and analyzing data, I would welcome facilities for analyzing different sorts of scale data like Likert scales, Thurstone scales, magnitude ratio scales, and Guttman scales, which I don't use myself.""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.""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 get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them.""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."

More KNIME Cons →

Pricing and Cost Advice
  • "The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well."
  • "In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos."
  • "As far as I understand, my company is not paying anything to use the product."
  • "I believe our cost is $1,000 per month."
  • More Darwin 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 →

    report
    Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
    765,386 professionals have used our research since 2012.
    Questions from the Community
    Ask a question

    Earn 20 points

    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
    27th
    Views
    507
    Comparisons
    260
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    4th
    Views
    11,490
    Comparisons
    7,931
    Reviews
    22
    Average Words per Review
    475
    Rating
    7.9
    Comparisons
    RapidMiner logo
    Compared 24% of the time.
    Microsoft  Power BI logo
    Compared 21% of the time.
    Alteryx logo
    Compared 14% of the time.
    Weka logo
    Compared 8% of the time.
    Also Known As
    KNIME Analytics Platform
    Learn More
    Overview

    SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.

    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
    Hunt Oil, Hitachi High-Tech Solutions
    Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company19%
    Financial Services Firm14%
    Government11%
    Real Estate/Law Firm11%
    REVIEWERS
    University23%
    Comms Service Provider17%
    Retailer14%
    Government9%
    VISITORS READING REVIEWS
    Manufacturing Company12%
    Financial Services Firm11%
    Computer Software Company10%
    Educational Organization8%
    Company Size
    REVIEWERS
    Small Business75%
    Large Enterprise25%
    VISITORS READING REVIEWS
    Small Business22%
    Midsize Enterprise10%
    Large Enterprise68%
    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.
    765,386 professionals have used our research since 2012.

    Darwin is ranked 27th in Data Science Platforms while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Darwin is rated 8.4, while KNIME is rated 8.2. The top reviewer of Darwin writes "Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". Darwin is most compared with Microsoft Azure Machine Learning Studio, Databricks and IBM Watson Studio, 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.