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KNIME Analytics Platform

KNIME Analytics Platform

Overview

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.

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Recent Reviews

TrustRadius Insights

KNIME Analytics Platform has proven to be a valuable tool for a wide range of users and industries. Novice data scientists appreciate the …
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Value for money!

10 out of 10
November 05, 2023
Internal Audit needs to identify the exceptions in the data to address the risks. These risks could be coming from IT or Business. So, we …
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Empowering People

9 out of 10
October 20, 2023
Incentivized
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 …
Continue reading
Read all reviews

Popular Features

View all 16 features
  • Connect to Multiple Data Sources (19)
    9.6
    96%
  • Data Transformations (19)
    9.4
    94%
  • Interactive Data Cleaning and Enrichment (19)
    9.0
    90%
  • Automatic Data Format Detection (19)
    9.0
    90%

Reviewer Pros & Cons

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Pricing

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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
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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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Features

Platform Connectivity

Ability to connect to a wide variety of data sources

9.1
Avg 8.5

Data Exploration

Ability to explore data and develop insights

8
Avg 8.4

Data Preparation

Ability to prepare data for analysis

8.3
Avg 8.2

Platform Data Modeling

Building predictive data models

7.9
Avg 8.5

Model Deployment

Tools for deploying models into production

7.3
Avg 8.6
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Product Details

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

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.Screenshot of the common node port types. Nodes can have multiple input ports and multiple output ports. A collection of interconnected nodes, using the input ports on the left and output ports on the right, constitutes a workflowScreenshot of the three ways nodes can be added to the workflow canvas; drag & drop, double click on the node in the node repository, or drop a connection into an empty area to display the quick nodes adding panel.Screenshot of how to set a workflow coach preferences.Screenshot of replacing a node into the workflow editor via drag & drop.Screenshot of the annotation field of a node, which is helpful for explainability and documenting of a workflow.Screenshot of the annotation function, which is helpful for explainability and documenting of a workflow.Screenshot of the space explorer, which is where users can manage workflows, folders, components, and files in a space, either local or remote on a KNIME Hub instance.Screenshot of the node repository, which is where currently installed nodes are available. Here, users can search for and then add a node from the repository into the workflow editor by drag & drop.Screenshot of the node monitor. This is located on the bottom part of the workbench and is especially useful to inspect intermediate output tables in the workflow.Screenshot of the KNIME Business Hub teams view. Resources can be owned by a team (e.g. spaces & the contained workflows, files, or components) so that team members can access these resources.Screenshot of the KNIME Collections view. Upskill users by providing selected workflows, nodes, and links about a specific, common topic.Screenshot of the KNIME Business Hub versioning. Track changes to workflows easily and in a transparent way.Screenshot of the KNIME Business Hub deployment options. After a workflow is uploaded to KNIME Hub different type of deployments can be created. For example: a Data App, schedule, API service, or trigger.Screenshot of the KNIME Business Hub Data Apps Portal. This page is available to every registered user. Consumers, for example, can access to this page to see all the data apps that have been shared with them, execute them at any time, interact with the workflow via a user interface, without the need to build a workflow or even know what happens under the hood.

KNIME Analytics Platform Videos

KNIME Analytics Platform Technical Details

Deployment TypesOn-premise
Operating SystemsWindows, Linux, Mac
Mobile ApplicationNo
Supported CountriesGlobal
Supported LanguagesEnglish

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.

Alteryx, Dataiku, and Qlik Sense are common alternatives for KNIME Analytics Platform.

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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Comparisons

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Reviews and Ratings

(69)
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Attribute Ratings

Reviews

(1-22 of 22)
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Score 10 out of 10
Vetted Review
Verified User
Nordax Bank uses the KNIME Analytics Platform to build risk and marketing models.
  • Machine learning models
  • Great support and user examples
  • Format that allows users to build very flexible workstreams
  • An optimization module that allows users to define constraints
Well-suited: 1. Machine learning tasks such as credit score and marketing response models 2. Integration with Python, R, and H2O offers great flexibility for users of different backgrounds to collaborate. 3. Share workstreams.

Less Appropriate: 1. Plot capabilities could in my mind be improved. The flexibility Tableau offers would be nice to also have in the KNIME Analytics Platform.
Mathias Denzin | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
KNIME Analytics Platform is the perfect tool for data loading, transformation, and analytics. The flexibility with Python and R scripting is incredible and replaces many manual data tasks. Every manual data transformation frequently done in Excel should be transformed into a KNIME Analytics Platform flow. In most cases, there is no need for manual steps in data analytics when using KNIME Analytics Platform. The available documentation and training are perfect for starters. The low-code-no-code approach gives unexperienced users a great chance to step up in the data analytics game. KNIME Analytics Platform is an everyday tool in my work life.
  • visual data flow creation
  • huge number of built-in nodes and function
  • very supportive community
  • webinars about use-cases and new functions
  • report design (a modern BIRT)
KNIME Analytics Platform Analytics is always the right tool for repetitive manual work (copy-paste data, use Excel formulas to transform data, export data, and create charts and reports). Creating a KNIME Analytics Platform Flow does not just remove the chance for errors but also provides documentation of the data transformation (the visual flow). All common data types and data sources are supported out of the box.
Score 8 out of 10
Vetted Review
Verified User
<div>We primarily use KNIME Analytics Platform as our go-to integration and ETL tool. The platform's makes it easy for our team to connect to diverse data sources, extract relevant information, and transform it into a format that suits our analytical needs. <br></div><div>We also use it as a reporting tool, creating data applications that various departments can consult and use on a daily basis.</div>
  • Seamless Integration with API, DBs, Tabular files
  • Robust ETL capabilities using or it's No code/Low code nodes
  • Automatize workflows
  • Unify ETL, ML and Reporting in the same framework
  • It's Open Source and has a strong community
  • Reporting, the reporting is lacking a lot in terms of customization, is really basic
  • Integration with Microsoft services
  • A SaaS option
Knime is suited for several scenarios:
  • ETL and Data Science Use Case scenarios for non technical people.
  • Data Science Democratization process, as with their new Server option called Business Hub it allows to create several teams within an organization where you can share components, WF, reports...
  • Automation of excel processes/reports that require a lot of time and manual interaction

Knime is less appropiate for:
  • Reporting capabilities, it's better to connect a reporting tool to it, Knime allows it.
  • Productionizing DS/ML models
November 05, 2023

Value for money!

Score 10 out of 10
Vetted Review
Verified User
Internal Audit needs to identify the exceptions in the data to address the risks. These risks could be coming from IT or Business. So, we collect data from various systems on daily basis and analyze the data to discover the exceptions and email them to the respective auditors for tracking and closing them. Almost 90 workflows are automated to run on daily basis. Beyond this, weekly, monthly and quarterly reports are generated and shared with the auditors. KNIME Analytics Platform is also used to prepare management dashboards to understand the risk trends. We also use KNIME Analytics Platform to conduct Data Science activities and identify the trends in the data. In many investigation cases, we need to relate data and identify the issues. We use KNIME Analytics Platform for this activity as well.
  • Extraction, Transformation and Loading
  • Integration with Python
  • Loading millions of records for analysis
  • Connectivity with Databases
  • Job Scheduling
  • Managing Date and Time functionality
  • Compatibility between Sever and AP
When you have data in tabular form and it needs to be analyzed, KNIME Analytics Platform is a best fit. If fact many times, my team goes to KNIME Analytics Platform to read the data from table rather than going to a DB tool such as SQL Developer because it is easy to manage and it also avoids fear of changing data at the source system unintentionally. Once the data is retrieved, play around with the data and simply close it. The team do not need to write many varieties of queries to extract the data. Data Apps can be improved, and it is considered one of the critical items. Similarly, there is no easy way to encrypt data and store in the database.
October 20, 2023

Empowering People

Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
KNIME Analytics Platform is a great productivity enhancing tool for any knowledge worker who wants to replace spreadsheets and VLOOKUPs in managing and blending data with systematic, repeatable procedures. KNIME Analytics Platform enables team managers and others who cannot perform development work and maintain coding skills on a daily basis due other responsibilities to quickly test their ideas and build prototypes. KNIME Analytics Platform is a good way to manage complex solutions even for seasoned coders due to the visual view of the workflow logic. And even if heavy lifting was performed by Python nodes instead of native KNIME nodes, the workflow view enables a citizen data scientist or even a business user to run and troubleshoot workflows independently. KNIME Integrated Deployment is a very innovative way for developing and deploying production workflows. There could be some weaknesses in relation to development work, at least in the soon legacy KNIME Server environment, where it is not possible to collaborate in the way of Git, but multiple team members could be working on the same workflow and deploy updates without knowing of each other.
Rob Blanford | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
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.
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.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
One of our clients has KNIME as a Data Wrangling and Data Science tool for internal data and process automation. KNIME is surprisingly easy to use and very powerful. You can go from automating the a process that reads data from a database and wrights it into a Excel file to a much more complex solution involving Machine Learning and complex Data Science solutions. I'm extremely happy with KNIME and how it bridges the gap between developers and the business users.
  • Data transformation
  • Data conversion
  • Data Wrangling
  • Workflow
  • Interpreting Excel files and translating it to CSV format
  • Nodes that do multiple transformations at the same time
ETL, TLE and Data Science. KNIME competes toe to toe with other tools like Alteryx. In fact it's more flexible and easier to use.
You can be a BA with basic skills in SQL and programing or a senior developer, KNIME will help you develop a easy to understand solution that will be easy to maintain
Asam Salim MCMI ChMC | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
As Head of Analytics for Eviden (an Atos company), I have built a strong relationship with KNIME as I wholeheartedly believe in the democratisation of best of breed analytics platforms, of which KNIME is one, that can significantly drive organisations to real business value from their data. It has a shallow learning curve and can drive immediate efficiencies in a short space of time, particularly in the management reporting space for those that are report producers.
As the KNIME Analytics Platform is open source, it integrates with other open source libraries and can accelerate organisations to delve into advanced analytics and AI in areas of prediction for example. There isn't a better time than today to unleash this platform across your user base and reap the value of enhanced quality of insights in parallel to increasing data literacy.



  • Visual programming as oppose to scripting encourages data analysts to reap deeper insights from their data
  • Large community contribution in extending the KNIME Analytics Platform into other areas of analytics, e.g. Text Analytics, Predictive Analytics, ML, etc.
  • Open source with periodic updates ensures it is equipped to deal with the most sophisticated data analytics use case
  • User interface has recently been improved to align with good practice on UX
The KNIME Analytics Platform can cater for anyone who has a role in analysing data. I am in the process of delivering a series of knowledge shares that will compliment our team of business consultants outside of our Insight Practice to take confidence that their analysis of data can benefit from a) automation of standard client reports, b) deeper insights into the data they are analysing.

The more advanced use of KNIME will continue to be demonstrated to our clients in the areas of a) data wrangling and automation and b) data science.
Score 10 out of 10
Vetted Review
Verified User
In our organization we need to frequently query and transform data for various deliverables. KNIME provides a robust set of capabilities to meet all of our data transformation needs.
  • KNIME is amazing at data transformation. KNIME contains every node imaginable to transform data in whichever way you need it. It is also a very stable program, reliable, and scales well when it process's large datasets. We reviewed numerous other programs in our organization before going with KNIME, and there were really no other programs that performed to the degree KNIME does. KNIME was a clear winner for us.
  • On the DB Query Reader node, it would be helpful if it had a graphical query building and editing interface, like KNIME's competitor platform has. It's not a deal breaker for our organization though as we develop the SQL in other application before importing into KNIME.
KNIME is great for simple to complex data transformations. I would recommend it if these types of data transformations are needed. Though if there is very simple data transformation that could be completed with just SQL alone, I would recommended using just that to a colleague.
Marc Cooley | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
KNIME Analytics Platform is the central data processing system we use in our consulting work with financial institutions. We have found the flexiaibity, integration with other systems such as SQLite, Python, etc. to be a great advantage. The self documenting nature of the GUI is awesome and eliminates a task no one wants to do (documentation). Access to AI machine learning tools is another bonus we are also exploiting to a great advantage.
  • 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.
  • The Excel reader node doesn't always reset. Sometimes the node has to be rebuilt or reconfigured to truely reset the node. This can trip you up if you're not aware of it.
  • Basic filtering in table view. Sure you just add a filter node, but it would be cool if the data tables worked more like tables in Excel where you can filter as well as sort.
KNIME Analytics Platform has vastly improved our effectiveness when working with large data sets. The self documenting GUI allows analysts to focus on what they are trying to accomplish, not complex code syntax. If we were to use traditional tools, like SQL, work would take much longer and it would be more difficult to collaborate both internally and with clients. Since KNIME Analytics Platform is database oriented, some spreadsheet functions are not supported, which is as it should be. For small data sets we often use Excel vlookup and pivot tables in place of KNIME Analytics Platform. If VBA code is requried, we go to KNIME Analytics Platform as we find VBA to be unstable in Excel.
Score 6 out of 10
Vetted Review
Verified User
Incentivized
Only limited in my role in my function. I have addressed big data issues where data need cleaning and transformation, fuzzy matching on customer data, mismatch of material numbers, sales representatives, bidding data. Good tool for artificial intelligence and analytics issues.

[KNIME Analytics Platform] has helped in automating the processes which were taking lot of manual work.
  • No license fee
  • Easy to understand and learn
  • Open architecture
  • Bunch of memory on your desktop
  • User interface is not that efficient
  • Lack of learning resources
1. Clean the big data and data transformation for data mapping and visualization purposes
2. Perform predictive analytics
3. Perform statistical modelling and analysis
4. It is not good for planning purposes
5. Not good for visualization and explain the business leaders about logic
6. customer segmentation, information retrieval and advanced analytic
7. Can perform risk analysis
Score 8 out of 10
Vetted Review
Verified User
Incentivized
[KNIME Analytics] had a slow start at the beginning as the company was heavily relying on the Alteryx designer or its server version. However, [KNIME] Analytics was a great discovery during the pandemic. Since it is open to all and free to use it is now replacing Alteryx software for many functionalities without paying the hefty charges.
  • No coding required to execute workflows, advanced excel knowledge is sufficient
  • Open source and connected to programming languages like Python and R for customization
  • Good community that can answer questions and provide sample workflows
  • User interface can be improved
  • Nodes repository has large number of functions but are difficult to locate and are sometimes confusing
  • Does a poor job on Data visualization
[KNIME Analytics] is greatly suited for repetitive tasks one has to perform in excel as it automates these mundane tasks. [KNIME Analytics] is also well suited for creating a seamless connection with other BI tools to enable hands-free file sharing. [KNIME Analytics] has improvements to make on the overall User interface, its data visualization package and advanced level of AI-related tasks such as text mining,
Score 9 out of 10
Vetted Review
Verified User
Incentivized
I use Knime to crunch large sets of data for marketing purposes. We take data from Google, our search console data, ahrefs, and other marketing platforms, manipulate it and then create reports and perform tasks. Recently, we did a site migration in which I used Knime to redirect all our backlinks a large task including more than 2 million data lines.
  • Large data set processing
  • Data manipulation
  • Server based execution
  • Manages multiple users/workflows
  • Data management
  • Simple tasks can take a long time.
  • Issues with data imports and merging multiple files.
Knime is perfect when you have a large data set that needs some manipulation, or you have a task involving multiple data sets that you are going to repeat again and again. It's a real time-saver in these cases. It is also ideal for work-sharing since there is a Knime Server available. It is not as good when you need some simple processing or manipulation of data. In some cases, you can spend hours building a workflow only to find simple issues that are blockers. When doing manipulation over large data sets that is a single step or a few steps, SQL or Python or another programming language is better suited.
Ivan Cui | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
My team uses KNIME Analytics Platform to build a variety of Data Science Pipelines. These KNIME workflows are then published through KNIME Server that can help hosting a front end for our end users across many different organizations. The KNIME workflows that we built have many different capabilities, ranging from data extraction, pre-processing, model training and optimization. We also build some self-services analytics platform using KNIME as well as automation tools.
  • Easy to use without much knowledge of coding.
  • Connection to other languages such as JS, R, Python, etc.
  • Workflow is displayed as connected nodes which makes it easy to troubleshoot and visualize.
  • Open-source.
  • Have a decent size community that supports Q&A.
  • Execution on other programming languages is slow.
  • Workflows are very big even building a very simple one due to caching and GUI.
  • Can frequently stop working and quit unexpectedly.
If you have a team of engineers or data scientists who do not like to code, KNIME can be a good platform to build quick and dirty pipelines. However if you are moving away from R&D to deployment, KNIME lacks the scalability compared to Python or R itself. When deploying, you can choose to output json or use their native front end from KNIME Server, but KNIME Server is not free.
Christopher Penn | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
KNIME is used as a bridge piece of software that connects multiple, disparate data sources into a single data pipeline for further analysis downstream. Some level of transformation is done in the processing, mainly for data cleansing, but most of that is left to custom code further on in the pipeline.
  • Connection to multiple data sources.
  • Unified interface for data and cleansing.
  • Cross platform interoperability.
  • Cumbersome UI.
  • Slow to load.
  • Memory/CPU hog.
KNIME is well suited for the data analyst that has multiple disparate data sources and needs to unify them, with a price point that is lower than some other enterprise packages. It's less well suited for smaller data pipelines or pipelines where a ton of custom coding and modification needs to be made.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
KNIME Analytics Platform is actively used as the Predictive Analytical tool in the whole organization across departments in all domains, from production to sales to marketing to IT. We already had reporting tools, ETL tools, visualization tools but a friendly, relatively easy to use with no programming knowledge was required to disseminate Predictive Analytics across the organization. KNIME Analytics Platform was chosen for this because anyone can use it with a little bit of training, and it does not require Computer Science knowledge/background. It is used to create a predictive model for various business domains and kinds of models, such as classification, regression, and clustering.
  • Graphical UI
  • Ease of Use
  • Speed: It works slow, especially the opening.
  • Degree of freedom and customization in default nodes.
KNIME Analytics Platform is best suited for an introduction to Data Science/Data Analytics. Since this area requires a somewhat computer science background because of data reading, retrieving, handling, preprocessing, model development, deployment is all carried out in some programming languages, and it is hard for a non-CS major to do these without knowing Python/R. This is where the KNIME Analytics Platform becomes handy. It contains graphical, drag-drop nodes that do these for you with no coding. Nodes are connected with one's output being another's input, as a workflow. Therefore end-to-end pipeline can be built with no coding. It also enables newcomers to the profession to follow up on what's going on in the pipeline, makes it easier to troubleshoot/debug, because it is very visual and intuitive. However, when high customization, sophisticated models, and speed is needed. KNIME Analytics Platform is less flexible and slow. So it is best for non-CS people, the business units.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
I have used KNIME for advanced data analytics and experiments in the AI (machine learning) area. I have also used this platform for running client data analysis in sourcing and sales areas, including running of prediction models.
This is a framework that allows you to start with simple tasks and gradually increase the analysis complexity.
After going repeatedly through several data sources with tons of data, the painful part has always been preparing and transforming the raw data for analysis. This can be automated and the data acquisition model can be saved and run repeatedly, saving a lot of time. Data cleansing and blending of tables is easy here. It also supports formats as JSON, XML, a quite frequent format nowadays.

Above all the platform and community is wide with hundreds of add-on modules. Frequently, someone has already solved a similar task as you. Before trying to model anything from scratch, it is a good idea to skim through modules and hopefully you can find a good one to use. And finally, it supports simple as well as complex analytics, including AI algorithms.
  • Great UX interface, easy connection of data sources, good handling of the analytical model, easy to modify.
  • It provides good level of control of what happens with your data in each step.
  • Great tool from data preprocessing, from analysis to visualization.
  • Great community and a lot of modules to reuse.
  • Supports machine learning - it is easy to configure and run.
  • It is Open Source!
  • If you are familiar with Python, you can use this easy programming language to add additional functions to your analytical model.
  • Automation - e.g. RapidMiner Studio provides a Turbo Prep function, where one can get to working on models more quickly (RapidMiner is not open source though)
  • KNIME does not replace a regular reporting tool - it is not meant to. However, if I have already spent some time developing a data acquisition and analytical model, it would be nice to be able to deploy, for example, a monitoring or reporting module that would process data autonomously and react accordingly.
If you are searching for a tool with a low total cost of ownership (TCO), that is easy to understand, and that comprises many prepared modules, KNIME is great. The tool is very intuitive with a lot of examples to learn. You can find a bit better tools, like RapidMiner Studio, but this is a paid, commercial solution. Yes, you can get a free RapidMiner license to process up to 50,000 lines of data, but this is not sufficient for serious work. Most of my use cases today require a bigger license, so KNIME is an attractive alternative price-wise.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
KNIME is used across the entire business. We primarily use it as an ETL tool and to act as the input for other BI software such as Tableau and QlikView. KNIME is also used for initial machine learning functions.
  • KNIME works better than most tools for ETL functions.
  • Easy to track the different steps
  • Easy to isolate and fix specific workflow steps.
  • 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.
KNIME is quite useful for initial data exploration and to share and discuss your process (workflow) with someone that does not how KNIME works. KNIME's visualisation tools can not be compared to most BI tools, because of the limited amount of available charts.
Rohit Narang | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We use KNIME due to its high-value predictive analytics and its ability to find patterns as a data mining tool. Its risk analytics are used in our department for the development of new models and model validation using time series for low default portfolios. Primarily for creating univariate and multivariate analysis and finding statistical significance of variables, and further correlations with a blend of statistical procedures in the banking industry.
  • For non-programming based functional users, it's a blessing as it doesn't require coding, programming skills to perform data mining. The full desktop version of KNIME is free and open source, with no limit to data.
  • Connect to Open source: It also offers excellent integration with a wide range of other open source software such as Python, R, Spark, and even ImageJ for image analysis.
  • Great Integration of functionalities: We never move data between applications/platforms to complete the project. Raw data is easily ingested in the tool, processed, can be performed statistics, summarised and exported to various formats.
  • Visualization can be improved further though it has been better with new versions, with a lot of scope available. However, connectivity to Tableau somehow overcomes this. Also, skilled resources are difficult to find for KNIME, due to other solutions having better penetration.
  • Knowledge of R/Python is required to fully use the statistical analysis (rather than just data mining). Also, memory usage is a problematic issue sometimes.
  • Not enough domain usage experience can be shared between KNIME users as well.
It is well suited for organizations having day to day advanced statistical procedures requirements. We use ANOVA, multivariate regression using time series modeling and several calibrations in our models for periodic change due to agile macro-sensitive economic forecasts.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
KNIME has been used as a training tool for students to use. This program acts as a basic way for students with limited bioinformatic and computational skills to solve big data problems. The program has been used for simulated drug discovery training purposes.
  • Easy to use
  • Open source; extra programs can be added easily
  • User interface can be crowded at times
KNIME Analytics Platform is well suited as a training program for students from a variety of computation backgrounds. It integrates well many of the common chemical and biological programs and data files into one program that can then be used to process and sort large inventories.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
We use KNIME Analytics for our client (one of the Big Four). The use of this platform is for NLP related tasks. Specifically for Information Retrieval. It is used by a division within the organisation.
  • Text processing is easily performed by the various extensions within this platform
  • Integrates multiple languages like Python, R , Java etc. all in one place
  • Also provides many options for text parsing like CoreNLP, OpenNLP
  • Documentation is poor
  • The developers are mostly not native English speakers therefore their verbiage is sometimes ambiguous in the given examples
It is good for data ingestion given various formats. Thereafter more time can be dedicated to data analysis and other downstream tasks.
Sergio Pulido Tamayo | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
We use KNIME across the whole organization. It is used to solve a wide range of business problems, from ETL and data integration, to advanced analytics and customer segmentation.
  • Connect to different data sources (uses JDBC)
  • Process large quantities of data
  • Integrate different machine learning frameworks and techniques
  • Use and integrate with cloud and big data environments
  • Does not have integration with Jupyter Notebooks
  • The tools for script writing and development are not easy to use or don't have many features
  • Memory usage is problematic some of the time
Perfect for training of non-expert users. It is well suited for any kind of analytics endeavor. It is appropriate for many information automation tasks.
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