KNIME Analytics Platform User Review
Nordax Bank uses the KNIME Analytics Platform to build risk and marketing models.
Starting at $0 per month
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What is KNIME Analytics Platform?
KNIME 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.
Recent Reviews
Popular Features
- Connect to Multiple Data Sources (19)9.696%
- Data Transformations (19)9.494%
- Interactive Data Cleaning and Enrichment (19)9.090%
- Automatic Data Format Detection (19)9.090%
Reviewer Pros & Cons
Senior Salesforce Solution Engineer
SMA Solar Technology America (Electrical & Electronic Manufacturing, 1001-5000 employees)
Pricing
KNIME Community Hub - Individual
$0
On Premise
KNIME Community Hub - Team
From €250
On Premise
per month Starts from 3 users
Entry-level set up fee?
- No setup fee
For the latest information on pricing, visithttps://www.knime.com/knime…
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
Break into Deep Learning for Image Data without Code
YouTube
Automating Financial Calculations with KNIME
YouTube
Leveraging ChatGPT in KNIME workflows
YouTube
Best Practices to Build KNIME Workflows
YouTube
Automating Out of Spreadsheet Hell with KNIME
YouTube
KNIME Software Short Demo
knime.wistia.com
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- Competitors
- Tech Details
- Downloadables
- FAQs
What is KNIME Analytics Platform?
KNIME empowers data users to build, collaborate, and upskill on data science. KNIME offers support across the data science life cycle, from creating analytical models to deploying them and sharing insights across the enterprise.
Users of KNIME tend to wear one of four hats:
Data experts can accelerate time to insight, collaborate with other disciplines, and empower upskilling across business functions. KNIME lets them:
* Connect to any data, access any analytic technique, and the choice to code in any language
* Get to insights faster using a low-code/no-code interface
* Eliminate repetitive, manual work by creating reusable, automated workflows
* Save and share Python scripts, analytical models, or data processes for reuse
* Provide blueprints that non-experts can learn and upskill from independently
* Speed up learning by accessing workflow samples by KNIME community members and experts
* Validate models with performance metrics and carry out cross validation to guarantee model stability
* Automatically document each step of the analysis visually
* Maintain models and fix mistakes more easily with version control, debugging, tracking, and auditing
Business & domain experts can access and blend data, perform advanced analyses, and deliver timely insights in a visual, interactive environment that eliminates the need to code. They can prep data faster and do deeper analyses because KNIME lets them:
* Connect to all data sources and access any file format in one visual environment.
* Transform data self-sufficiently in the same visual environment without IT dependency
* Use visual workflows from others as blueprints to get started faster
* Automate repetitive data tasks like data prep and reporting with reusable workflows
* Minimize the time to spot and fix errors with each step of the analysis clearly visible, and track changes with version control
* Access thousands of self-explanatory nodes to perform the actions needed on data
* Create workflows of any complexity by joining nodes together via drag and drop
End users can get insights with custom-built, interactive data apps without needing to know how to code or build analytical models. They can make faster, data-driven decisions with advanced analytics at their disposal because KNIME lets them:
* Interact with analyses of any complexity level with a data app UI
* Access data apps via the browser with a secure connection or shareable link
* Identify patterns with job-relevant data apps and provide feedback to improve the model
* Lower the barrier between them and data science teams, enhancing analytics output accuracy
* Choose to get insights from simple dashboards or complex, interactive visualizations
* Explore data and perform ad hoc analyses using interaction points within data apps
* Avoid vendor lock-in and adapt to changing business needs with an extensible platform
MLOps and IT teams use KNIME to securely deploy, manage, and scale with a single installation while ensuring enterprise-grade security and governance. The platform enables them to:
* Safely deploy and monitor models from one single place
* Ensure adherence to best practices
* Meet enterprise needs while ensuring data security and governance
* Securely productionization data science at scale
KNIME Analytics Platform Features
Platform Connectivity Features
- Supported: Connect to Multiple Data Sources
- Supported: Extend Existing Data Sources
- Supported: Automatic Data Format Detection
Data Exploration Features
- Supported: Visualization
- Supported: Interactive Data Analysis
Data Preparation Features
- Supported: Interactive Data Cleaning and Enrichment
- Supported: Data Transformations
Platform Data Modeling Features
- Supported: Multiple Model Development Languages and Tools
- Supported: Automated Machine Learning
- Supported: Single platform for multiple model development
Model Deployment Features
- Supported: Flexible Model Publishing Options
KNIME Analytics Platform Screenshots
KNIME Analytics Platform Videos
Watch a short animated video, on how Katie uses KNIME to make sense of data within her organization.
Watch a short demo covering both of KNIME's software products: KNIME Analytics Platform and KNIME Hub.
Watch the recap of the Fall Summit 2022, where the community discusses what they love most about KNIME.
KNIME Analytics Platform Competitors
KNIME Analytics Platform Technical Details
| Deployment Types | On-premise |
|---|---|
| Operating Systems | Windows, Linux, Mac |
| Mobile Application | No |
| Supported Countries | Global |
| Supported Languages | English |
KNIME Analytics Platform Downloadables
Frequently Asked Questions
KNIME 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.
KNIME Analytics Platform starts at $0.
Reviewers rate Extend Existing Data Sources highest, with a score of 10.
The most common users of KNIME Analytics Platform are from Enterprises (1,001+ employees).
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Reviews and Ratings
(68)
Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Recommendations
KNIME Analytics Platform has proven to be a valuable tool for a wide range of users and industries. Novice data scientists appreciate the platform's no-code environment, which allows them to focus on the methodology and intent of their analyses without getting bogged down by syntax errors. The intuitive visual node structure is particularly beneficial for non-technical clients who prefer a user-friendly interface over coding, enabling them to find solutions quickly. Experienced data scientists also find value in KNIME Analytics Platform, as it allows them to work in their preferred language using the R and Python nodes.
The platform's flexibility and ability to integrate well with other systems like SQLite and Python make it suitable for consulting work with financial institutions. KNIME Analytics Platform also provides self-documenting capabilities, eliminating the need for manual documentation tasks. Additionally, the platform offers access to AI machine learning tools that prove advantageous for data analysis and finding patterns. It is commonly employed in risk analytics and model development within the banking industry, facilitating univariate and multivariate analysis as well as determining the statistical significance of variables.
Beyond finance, KNIME Analytics Platform finds utility in advanced data analytics and AI experiments. It is used for data analysis in sourcing and sales areas, including running prediction models. The platform allows users to start with simple tasks and gradually increase analysis complexity, making it accessible for organizations of varying skill levels. By automating data preparation and transformation, KNIME Analytics Platform saves precious time in data analysis processes. It supports various data formats like JSON and XML, further enhancing its versatility.
Furthermore, the KNIME community boasts hundreds of add-on modules that provide existing solutions for similar tasks, making it easier to tackle complex projects. With support for both simple and complex analytics, including AI algorithms, the platform caters to diverse analytical needs across industries. For marketing purposes, KNIME Analytics Platform excels at crunching large sets of data by facilitating data manipulation, report creation, and running prediction models. Its efficacy has earned it a reputation as a best-of-breed analytics platform that can drive real business value from data.
Many users commend KNIME Analytics Platform for its shallow learning curve, enabling immediate efficiencies in management reporting. The platform integrates seamlessly with other open-source libraries, empowering users to leverage advanced analytics and AI capabilities. It serves as a bridge between multiple data sources, facilitating data cleansing and transformation. Consequently, KNIME Analytics Platform is widely used across organizations for ETL, data integration, advanced analytics, and customer segmentation.
Notably, KNIME Analytics Platform has emerged as a cost-effective alternative to Alteryx software for many functionalities. Its applications extend beyond data analysis and into internal audit, where it helps identify exceptions in data, generate reports, and prepare management dashboards. With its data science capabilities, KNIME Analytics Platform assists in investigating big data issues and automating processes.
The platform's drag and drop interface and visual management of software code allow users to quickly test concepts and build prototypes of data pipelines, machine learning solutions, and data apps. Fast access to and blending of data from various sources, including databases, APIs, and flat files, is made possible by KNIME Analytics Platform. The wide range of pre-built nodes covering machine learning algorithms, combined with Python integration and shared components, ensures that users have the tools they need to fill any gaps in their workflows.
As organizations strive to go beyond spreadsheets and traditional BI systems, KNIME Analytics Platform fills the gap by providing professional-level data processing and data science capabilities to anyone. It not only offers standalone solutions but also provides collaboration and automation features through the server solution, allowing users to automate tasks and make data apps accessible to anyone within the organization. Whether it's building data science pipelines, automating tasks, or creating self-service analytics platforms, KNIME Analytics Platform proves to be a versatile tool that meets various business needs.
From NLP-related tasks like information retrieval to addressing customer segmentation challenges in marketing departments, KNIME Analytics Platform has become an indispensable tool for organizations across domains. Its powerful capabilities for data transformation make it a robust choice for meeting various data transformation needs within organizations.
The ease of use and power of KNIME Analytics Platform have garnered praise from users who appreciate its ability to automate simple processes or develop complex solutions involving machine learning and data science. With its deep integration with other open-source libraries and its ability to handle large datasets effectively, KNIME Analytics Platform empowers users to drive innovation and extract valuable insights from their data.
Users commonly recommend the following when it comes to KNIME:
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Try KNIME for beginners in data analytics. Users suggest using KNIME as a starting point for those new to data analytics. They feel that it provides a good foundation and helps in better understanding data sets and features.
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Utilize KNIME for data cleansing. KNIME's drag and drop feature is often praised by users, who recommend it for data cleansing tasks. They find this feature beneficial and user-friendly.
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Consider switching to other analytics tools once proficient. Several reviewers recommend using KNIME as an open source software specifically for beginners in data analytics. However, they suggest transitioning to other tools like Alteryx or similar options once users have gained proficiency with KNIME.
Overall, users appreciate KNIME's suitability for beginners in data analytics, its effectiveness in data ingestion and experimentation, as well as its role in disseminating knowledge within a company. They also mention the importance of mastering R/Python for effective implementation and note that while KNIME has a lot of examples to learn from, it does not replace regular reporting tools.
Attribute Ratings
Reviews
(1-3 of 3)Companies can't remove reviews or game the system. Here's why
October 20, 2023
Empowering People
We use KNIME for three overlapping use cases. (1) With its drag and drop interface and visual management of software code it is a great tool for quick testing of concepts and building prototypes of data pipelines, machine learning solutions and data apps. With KNIME Analytics Platform, it is very fast to access and blend data from various sources including databases, APIs and flat files. KNIME's pre-built nodes cover a range of machine learning algorithms and associated procedures and where they fall short, its Python integration and shared components are likely to cover the gap. (2) As a free and accessible, but yet extremely powerful data tool KNIME Analytics Platform brings professional-level data processing and data science into the hands of anyone who wants to develop data skills beyond spreadsheets and BI systems. As the central analytics team, we can promote the tool to everyone whether they eventually became a user or not, without incremental cost. (3) The commercial product, KNIME Server/Business Hub enables turning the solutions developed in (1) and (2) into automated jobs and data apps accessible to anyone in the organisation.
- Easy access to powerful data wrangling capabilities to business users and citizen data scientists
- Simple management of complex analytical processes and user interfaces due to the visual workflow approach
- Straight forward integration with Python for additional capabilities
- Data Apps (KNIME Server/Business Hub) have the potential of moving self-service analytics and collaboration between business teams from creating and sharing BI dashboards into real applications with complex backends and rich user inputs
- The visualisation nodes that KNIME Analytics Platform offers out-of-the-box lack variety and configuration options to optimise their usability and looks for different use cases. However, the JavaScriptView and PythonView nodes together with the ability of using CSS styling should in principle provide boundless opportunities but are not necessarily accessible for those looking for a No Code/Low Code approach (also, the JavaScript nodes would benefit from similar package management approach to the Python integration). There are some user-driven developments and component nodes available on the KNIME Hub that improve the basic visualisation functionalities, but perhaps this is an area the KNIME team could also focus on with new nodes and components. One way of boosting development could be competitions for the user community focusing on visualisation approaches.
- Similarly, and related to the visualisation capabilities, the capabilities for creating Data Apps could be improved. More refined and intuitive user interaction within component views would require additional functionality. It would also be important to have more overall control of the app display and be able to create apps that do not follow the generic flow with standard [Next] and [Close] buttons, to disable the showing of the progress bar (which sometimes weirdly moves backwards rather than forwards) and to generate apps that can use the whole screen with fully customisable backgrounds. The objective should be to enable developing apps that the end-users will find intuitive and familiar based on their experience of mobile and other apps rather than expect users to adapt to certain idiosyncrasies of KNIME Apps.
85%
8.5
75%
7.5
85%
8.5
86.66666666666666%
8.7
90%
9.0
- Intuitive data wrangling on KNIME Analytics Platform and deployment of data pipelines on the Server enabled us to insource previously outsourced BI development to a data science team residing within the business division, and subsequently exploring much more value adding solutions of ML/AI by combining our domain knowledge and technical skills.
- Alteryx and Dataiku DSS
As a commercial product Alteryx is more polished and can be even easier for a beginner, but KNIME beats Alteryx in functionality and performance. Dataiku takes the integration with Python and Git further than KNIME but isn't at the level of Alteryx and KNIME with its No Code/Low Code interface. In comparison to both Alteryx and Dataiku, KNIME is more versatile and significantly cheaper to deploy.
10
3 (30%) of the users are in the data science/analytics team sitting in the business area. One person (10%) is the KNIME Server Admin at IT, with the rest of the users 6 (60%) being business users who use KNIME Desktop for personal automation and productivity (local workflows with lasting and widespread utility are deployed and managed on the KNIME Server by the central data science/analytics team). Additionally, training has been provided for all staff on moving from Excel to KNIME in the past. Around 40% of the current staff of the 40-strong business area have some experience of KNIME and more training on data wrangling is planned for the future.
4
One person in the IT supports connectivity of KNIME to other in-house data infrastructure. A three person data science/analytics team with domain expertise and data and IT skills supports business users in utilising the tool in their day-to-day activities. Supporting of the business users requires understanding of their analytical objectives and processes, knowledge about the relevant data sources, the ability to convert spreadsheet-based processes into robust and streamlined pipelines and the skill to explain the conversion in layman terms.
- Personal productivity
- Orchestration of analytical procedures
- Productionising data science
- Exception management as an integral part of a data workflow
- The KNIME Business Hub may enable business users to share components and workflows
- With improvements to Data App functionalities, KNIME can have an important role in end-user facing interfaces
- Other user-facing apps than those running on KNIME can utilise the KNIME Server/Business Hub's API endpoint and use KNIME workflows as their backend
No
- Integration with Other Systems
- Ease of Use
KNIME enabled expanding self-service by business teams from consumption of dashboards and managing spreadsheets to repeatable data pipelines and machine learning applications.
I wouldn't change anything. There aren't other tools that would combine the ease of use, powerful functionality and cost-free nature of the KNIME Analytics Platform.
- Implemented in-house
Yes
We started by using individual installations of the open-source KNIME Desktop and after proving the value of the tool, the Server product was subscribed to.
Change management was a big part of the implementation and was well-handled
KNIME was the first tool in the organisation that would provide a database access to business teams and enable them to develop powerful data tools and apps independent of the IT department. This required us to partly redefine the roles and responsibilities of the business teams vis a vis the IT department, but this was all done in collaboration between the teams and solutions were found quickly.
- We encountered crashes of the KNIME Server when utilising certain memory-intensive Machine Learning nodes. It took longer than expected to fix the issue given the complexity of the Server product and limited Windows expertise at KNIME
We subscribe to KNIME Server which includes dedicated user support.
- Drag and drop data pipeline and machine learning development in general
- Reading data from Excel spreadsheets ignoring header rows or extra columns
- Integrated deployment of production solutions
- There are some legacy issues with nodes and packages developed at different times or by different teams that can have overlapping functionality and that may not work together even if they are seemingly functionally related
- The visualisation nodes are not always intuitive to use
No
- Azure SQL Server
- Anaconda Python
- Microsoft Excel
- File import/export
- API (e.g. SOAP or REST)
- Javascript widgets
- ETL tools
October 17, 2023
An incredibly comprehensive and powerful data analytics platform that is somehow free
The KNIME Analytics Platform provides a comprehensive set of tools for addressing the data manipulation and data science issues we encounter. Internally we use it for training new data scientists, building awareness of the data science workflow and data manipulation with non-technical staff. We also use it on our own data projects. The no-code environment allows us to focus on the methodology and intent of analyses with novice users without them encountering errors in syntax as they would if they were learning to code at the same time. However, the R and Python nodes allow experienced data scientists to work in their preferred language as well as allowing us to scaffold the learning of new data scientists in those languages when it becomes advantageous. We find non-technical clients will engage with the visual node structure much more than code, which helps us get to a solution more quickly. We can deliver stand-alone solutions to clients with confidence that we are not tying them into an expensive vendor relationship. Clients value that they can give access to all of their users at no licencing cost. Where collaboration and automation is required, KNIME Analytics Platform offer an extremely competitive server solution.
- Connectivity to an array of data sources and joining the data
- Rapid prototyping across data science use cases
- Making data science explainable to non-experts
- Democratising data - KNIME Analytics Platform allows everyone access to powerful analysis techniques
- Providing simple access to powerful external data science tools such as H2O and hyperscalers
- The previous UI of KNIME Analytics Platform provided easy access to a wide range of examples which is an extremely valuable resource for understanding how to break down a problem in KNIME Analytics Platform and provide accelerated delivery for similar use cases. Access to these resources doesn't seem possible at the moment in the new UI, but I believe it is being actively worked on. The examples are still available in the platform, but presently you need to switch back to the old UI.
95%
9.5
75%
7.5
100%
10.0
93.33333333333334%
9.3
90%
9.0
- KNIME Analytics Platform is free and our investment in training time has been paid back many times over when using the software for rapid prototyping and implementing our own analysis
- On one occasion we deployed KNIME Analytics Platform and Python with a client. Due to their secure environment, it was prohibitively expensive in time and capital to add Python packages to account for additional requirements as the project progressed and we switched entirely to KNIME Analytics Platform due it its comprehensive set of features. Without KNIME Analytics Platform the project would have been halted and resulted in a loss of >£40k in revenue.
There are two aspects which put KNIME Analytics Platform ahead of other products. Firstly the fact that KNIME Analytics Platform comes at no cost and no restrictions on its use is an instant winner for any organisation wanting to democratise their data. It means that a client is free to install it on as many machines as they wish without worrying about costs, the number of seats required or payment models or procurement negotiation. It also means that we are not building costs into our clients business.
Secondly, KNIME Analytics Platform has a very comprehensive set of tools for importing/exporting data, data manipulation and data science. Some products offer analytics packages on top of their base offering at additional cost and they are still not as comprehensive as what you get with KNIME Analytics Platform for free. For some types of analysis you may require to download additional packages with KNIME Analytics Platform, but its invariably at no cost, those packages are kept out of the main download to keep the size down. Due to the easy integration with R and Python, I view KNIME Analytics Platform as also having the capabilities of those languages too. This has helped me in the past with seamlessly importing a rare filetype and using very specific models not directly available in KNIME Analytics Platform.
Secondly, KNIME Analytics Platform has a very comprehensive set of tools for importing/exporting data, data manipulation and data science. Some products offer analytics packages on top of their base offering at additional cost and they are still not as comprehensive as what you get with KNIME Analytics Platform for free. For some types of analysis you may require to download additional packages with KNIME Analytics Platform, but its invariably at no cost, those packages are kept out of the main download to keep the size down. Due to the easy integration with R and Python, I view KNIME Analytics Platform as also having the capabilities of those languages too. This has helped me in the past with seamlessly importing a rare filetype and using very specific models not directly available in KNIME Analytics Platform.
No
- Scalability
- Integration with Other Systems
- Ease of Use
- Other
Price. It's free, which meant no headaches with Procurement or licensing, anyone can download it, install it and use it without restriction.
I wouldn't change it. We talked to a variety of organisations working in the space, engaged in demos, tutorials and conducted comparisons on price and capability. KNIME Analytics Platform did not win out in every category, but it was the clear overall winner of our selection process and we have no regrets in making it our go-to no-code solution.
- Rapid prototyping
- Comparing model performance
- Connecting to data
- The concept of "flow variables" was a little difficult to grasp
- Regex is a bit limited so I often choose to use an R or Python node, so it's only a minor inconvenience.
- Building reports was a bit difficult in the past, but it's not something I've ever needed for a project and might have changed since I last tried.
No
October 04, 2019
Knime, your one-stop-ETL-software-shop
- KNIME works better than most tools for ETL functions.
- Easy to track the different steps
- Easy to isolate and fix specific workflow steps.
80%
8.0
30%
3.0
70%
7.0
52.5%
5.3
N/A
N/A
- Positive: We were able to replace our costly previous ETL tools.
- Positive: The initial software evaluation phase was free.
KNIME is our preferred ETL tool, because multiple people can work on the same workflow at the same time.
15
Retail, Marketplace, Supply Chain, Finance
2
Business analytics, Tech savvy, BI savvy
- ETL
- Workflow sharing
- Standardized processes
- Basic machine learning functions
Yes
We replace in-house products, because KNIME had more functions and it is easier to maintain.
- Price
- Product Features
- Product Usability
Knime had the most features available compared to the considered alternatives.
I would not change the evaluation process.
- Implemented in-house
No
Change management was minimal
No specific lessons. Everyone readily accepted the new software.
- The KNIME server setup.
- Ensuring KNIME server up-time.
No
A vital feature kept on crashing when I tried to download and install it. I contacted KNIME support and they guided me in the steps required to uninstall and reinstall the required features.
- Basic ETL functions.
- SQL integration.
- Google Sheets integration.
- Visualization.