KNIME Analytics Platform

4.6
Overall rating
Reviews

4.6
Overall rating
Reviews

About KNIME Analytics Platform

KNIME empowers all data users to build, collaborate, and upskill on data science. KNIME offers complete 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. They can spend time where it matters because 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, all 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 instant 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 an intuitive data app UI * Easily 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. KNIME 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 Software - 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.
KNIME Analytics Platform Software - The three ways nodes can be added to your 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.
KNIME Analytics Platform Software - 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.
KNIME Analytics Platform Software - 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.
KNIME Analytics Platform Software - the KNIME Business Hub versioning. Track changes to workflows easily and in a transparent way.
KNIME Analytics Platform Software - 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.
KNIME Analytics Platform video
KNIME Analytics Platform Software - 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. - thumbnail
KNIME Analytics Platform Software - The three ways nodes can be added to your 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. - thumbnail
KNIME Analytics Platform Software - 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. - thumbnail
KNIME Analytics Platform Software - 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. - thumbnail
KNIME Analytics Platform Software - the KNIME Business Hub versioning. Track changes to workflows easily and in a transparent way. - thumbnail

KNIME Analytics Platform pricing

KNIME Analytics Platform has a free version and does not offer a free trial. KNIME Analytics Platform paid version starts at EUR 0.00.

Starting Price:
EUR 0.00
Free Version:
Yes
Free trial:
No

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KNIME Analytics Platform Reviews

Feature rating

Value for Money
4.7
Functionality
4.4
Ease of Use
4.5
Customer Support
3.9
5 reviews of 25 View all reviews
Rochelle
Rochelle
  • Industry: Information Technology & Services
  • Company size: 10,000+ Employees
  • Used Daily for 6-12 months
  • Review Source
Value for Money
0
Functionality
5
Ease of Use
5
Customer Support
0

5
Reviewed on 28/01/2023

Well created open source for data analysis!

Pros

One of the pros is of course doesn't require license fee. It is also an open source that can connect to Python and R that is capable of customization. Need to mention also the good community support.

Cons

It took time to understand the functionalities and familiarize the user interface.

Ferhat
  • Industry: Information Technology & Services
  • Company size: 5,001–10,000 Employees
  • Used Daily for 2+ years
  • Review Source
Value for Money
3
Functionality
3
Ease of Use
5
Customer Support
3

4
Reviewed on 25/01/2020

Data Science 101 Platform for non-IT people

It was the tool I learned the Data Science in the first place. So it is really good and intuitive with its graphical interface. For example you understand train-test split very well because you literally see the split as you work on it. As I progressed and needed more functions and more custom solutions, I started using Python scripts and solved it like that. So it gave me all these abilities.

Pros

- Its ease of use makes it possible for non-IT, non-developer, non-CS background people to make data manipulation, preprocessing, mining, visualization and modelling.
- It has a graphical interface with nodes and connections so that you don't need to know Python/R to make predictive models or association rules/recommendation systems.
- There's a vast library of functions
- Even more functions are created by the community so non-existing customized functions are created by the community, via existing functions.
- The visual flow of data makes it easy to understand and interpret it.
- It teaches the CRISP-DM methodology in an intuitive way thanks to its graphical user interface
- It can connect to SQL and similar servers so that the data can be read directly.
- It is possible to write own Python/R script for custom needs.

Cons

- Custom needs are hard to carry out.
- Functions have limited abilities and parameters
- Data visualization is weak and relatively primitive
- Model development is easy but deployment is hard
- It is very slow unfortunately and I think this is KNIME's most important drawback

Alternatives Considered

SAS Enterprise Miner and IBM SPSS Statistics

Reasons for Switching to KNIME Analytics Platform

Not only other options were very expensive and KNIME was free but also KNIME came with much more functionality, compared to other end-user packages.
Verified Reviewer
  • Industry: Health, Wellness & Fitness
  • Company size: 5,001–10,000 Employees
  • Used Weekly for 6-12 months
  • Review Source
Value for Money
5
Functionality
4
Ease of Use
3
Customer Support
2

4
Reviewed on 01/05/2020

Solid Platform for Small Datasets and Broad Data Connectivity

The two main reasons we used KNIME were to process and prep data, then to conduct machine learning by training models and processing predictions. KNIME is great with data prep and blend as long as the data set is small to medium in size (< 4GB). There were areas where we struggled and that was when models were more complex (> 50 variables) and being able to deploy and schedule jobs. We had to download JDBC drivers for our database connections, which was not something we had to do with other platforms.

Pros

There is a wide range of tools to process and prep data in the platform natively and additional tools that can be download within the platform. The ability to customize the settings for most of the tools allows the user to adjust the output. Even more technical settings, like hyperparameter tuning, can be done in the tool UI. There are numerous input and output options and types.

Cons

Pulling in very basic files, like Excel spreadsheets can be a bit challenging where other platforms handle files with ease. Also, database connections are not seamless. The Java memory errors also limit the size of data that can be processed without making manual adjustments to settings. Lastly, not being a cloud-based platform, processing big data is very time-consuming.

Reasons for Switching to KNIME Analytics Platform

In the end, we moved away from KNIME and chose Alteryx.
Verified Reviewer
  • Industry: Higher Education
  • Company size: 51–200 Employees
  • Used Daily for Free Trial
  • Review Source
Value for Money
5
Functionality
4
Ease of Use
4
Customer Support
0

5
Reviewed on 26/09/2020

Great for all types of data scientists

I have had a very positive experience with KNIME and like it a lot more than other drag and drop machine learning tools I have tried out.

Pros

Some drag and drop tools for machine learning are really limited, but KNIME is not. There are a ton of capabilities of the tool that are built in, and there are even more that are available online, like AutoML. It gives citizen data scientists the ability to create good models without knowing a programming language, and it increases the bandwidth of actual data scientists by allowing them to easily create more models and experiments.

Cons

Of course, it is more limited than a programming language, and if you're familiar with building models programmatically, there is a learning curve that will slow you down and limit you at first.

Alternatives Considered

Microsoft Power BI

Reasons for Switching to KNIME Analytics Platform

The pricing model for KNIME was better for us, because the free version includes a lot more than the others, and right now, helping clients get started for free and easily is the most important part to us.
Sasha
  • Industry: Information Technology & Services
  • Company size: 5,001–10,000 Employees
  • Used Weekly for 1+ year
  • Review Source
Value for Money
0
Functionality
5
Ease of Use
4
Customer Support
0

5
Reviewed on 12/01/2024

Using KNIME for reporting

Good and would recommend to non technical professionals as well

Pros

KNIME allowed me to pull data from large google sheets and manipulate them in a clear and easy way. The visual representation of each node makes it really easy to use and understand even for people without a background in data analytics. The KNIME website also provides a lot of resources on using the platform

Cons

Very large google sheets containing a lot of data cannot always be extracted due to the size.

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