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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 …
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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

(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!

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:

  1. 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.

  2. 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.

  3. 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

(1-22 of 22)
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Score 10 out of 10
Vetted Review
Verified User
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 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
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
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
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
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
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
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
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 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
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] 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
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
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 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 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
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 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
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 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.
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