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.
TrustRadius Insights
The Swiss army knife for data jobs
Open Source outstanding tool with room for imporvement
Value for money!
Empowering People
An incredibly comprehensive and powerful data analytics platform that is somehow free
A tool that bridge the gap between business and technology
KNIME Analytics Platform is for everyone, with little to no experience needed!
KNIME - the only Data Analytics Platform you need - and it's free!
KNIME Analytics Platform Makes Life Easier and More Fun
KNIME ANALYTICS - Great and free tool for beginners
An open source, well created data science software for all analysts
KNIME Offers Large-Scale Data Manipulation for Complex Data Streams
KNIME Review from a daily user
KNIME: Great value, great compatibility
Awards
Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards
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
Pricing
KNIME Community Hub Personal Plan
$0
KNIME Analytics Platform
$0
KNIME Community Hub Team Plan
€99
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
Break into Deep Learning for Image Data without Code
Automating Financial Calculations with KNIME
Leveraging ChatGPT in KNIME workflows
Best Practices to Build KNIME Workflows
Automating Out of Spreadsheet Hell with KNIME
Features
Platform Connectivity
Ability to connect to a wide variety of data sources
- 9.6Connect to Multiple Data Sources(19) Ratings
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
- 10Extend Existing Data Sources(10) Ratings
Use R or Python to create custom connectors for any APIs or databases
- 9Automatic Data Format Detection(19) Ratings
Automatic detection of data formats and schemas
- 7.9MDM Integration(8) Ratings
Integration with MDM and metadata dictionaries
Data Exploration
Ability to explore data and develop insights
- 8Visualization(18) Ratings
The product’s support and tooling for analysis and visualization of data.
- 8Interactive Data Analysis(18) Ratings
Ability to analyze data interactively using Python or R Notebooks
Data Preparation
Ability to prepare data for analysis
- 9Interactive Data Cleaning and Enrichment(19) Ratings
Access to visual processors for data wrangling
- 9.4Data Transformations(19) Ratings
Use visual tools for standard transformations
- 7.4Data Encryption(7) Ratings
Data encryption to ensure data privacy
- 7.4Built-in Processors(8) Ratings
Library of processors for data quality checks
Platform Data Modeling
Building predictive data models
- 9.4Multiple Model Development Languages and Tools(17) Ratings
Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.
- 8.2Automated Machine Learning(17) Ratings
Tools to help automate algorithm development
- 9.2Single platform for multiple model development(18) Ratings
Single place to build, validate, deliver, and monitor many different models
- 5Self-Service Model Delivery(8) Ratings
Multiple model delivery modes to comply with existing workflows
Model Deployment
Tools for deploying models into production
- 8.6Flexible Model Publishing Options(11) Ratings
Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.
- 5.9Security, Governance, and Cost Controls(4) Ratings
Built-in controls to mitigate compliance and audit risk with user activity tracking
Product Details
- About
- Integrations
- Competitors
- Tech Details
- Downloadables
- FAQs
What is KNIME Analytics Platform?
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
- Supported: Self-Service Model Delivery
Model Deployment Features
- Supported: Flexible Model Publishing Options
- Supported: Security, Governance, and Cost Controls
KNIME Analytics Platform Screenshots
KNIME Analytics Platform Videos
KNIME Analytics Platform Integrations
- Snowflake
- Amazon Athena
- Amazon S3 (Simple Storage Service)
- Apache Hive
- Azure Blob Storage
- Azure Data Lake Storage
- Azure Databricks
- Databricks Data Intelligence Platform
- Google Cloud Storage
- Google Sheets
- Google BigQuery
- Google Drive
- Microsoft Power BI
- Microsoft SQL Server
- Microsoft SharePoint
- MongoDB
- MySQL
- PostgreSQL
- Neo4j
- Oracle Database
- SAP HANA Cloud
- Apache Spark
- Tableau Server
- Amazon EMR (Elastic MapReduce)
- Azure Synapse Analytics
- Azure SQL Database
- Amazon Comprehend
- Amazon Personalize
- Azure OpenAI Service
- OpenAI API
- Cloudera Data Platform
KNIME Analytics Platform Competitors
KNIME Analytics Platform Technical Details
Deployment Types | On-premise, Software as a Service (SaaS), Cloud, or Web-Based |
---|---|
Operating Systems | Windows, Linux, Mac |
Mobile Application | No |
Supported Countries | Global |
Supported Languages | English |
KNIME Analytics Platform Downloadables
Frequently Asked Questions
Comparisons
Compare with
Reviews and Ratings
(66)Community Insights
- 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:
-
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.
-
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.
-
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)KNIME Analytics Platform User Review
- 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
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.
The Swiss army knife for data jobs
- 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)
Open Source outstanding tool with room for imporvement
- 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
- 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
- Reporting capabilities, it's better to connect a reporting tool to it, Knime allows it.
- Productionizing DS/ML models
Value for money!
- 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
Empowering People
- 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.
An incredibly comprehensive and powerful data analytics platform that is somehow free
- 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.
A tool that bridge the gap between business and technology
- 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
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
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 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.
- 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 Analytics Platform Makes Life Easier and More Fun
- 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 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
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
- 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
- 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 Review from a daily user
- 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.
KNIME: Great value, great compatibility
- Connection to multiple data sources.
- Unified interface for data and cleansing.
- Cross platform interoperability.
- Cumbersome UI.
- Slow to load.
- Memory/CPU hog.
- Graphical UI
- Ease of Use
- Speed: It works slow, especially the opening.
- Degree of freedom and customization in default nodes.
Consultant's best (free) friend
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.
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.
KNIME blended With R skills Is a great GUI Based Analytics & Mining Tool, Specially for Advanced Statistical Usage
- 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.
Knime for interactive training purposes
- Easy to use
- Open source; extra programs can be added easily
- User interface can be crowded at times
KNIME Analytics: Developer Review
- 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
- 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