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Cloudera Data Science Workbench vs KNIME comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Cloudera Data Science Workb...
Ranking in Data Science Platforms
23rd
Average Rating
7.0
Reviews Sentiment
6.9
Number of Reviews
2
Ranking in other categories
No ranking in other categories
KNIME
Ranking in Data Science Platforms
2nd
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
59
Ranking in other categories
Data Mining (1st)
 

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Cloudera Data Science Workbench is 1.3%, down from 1.7% compared to the previous year. The mindshare of KNIME is 11.7%, up from 9.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Ismail Peer - PeerSpot reviewer
Useful for data science modeling but improvement is needed in MLOps and pricing
If you don't configure CDSW well, then it might be not useful for you. Deploying the tool can vary in complexity, but most of the time, it's relatively simple and straightforward. Triggering a job from data to production is easy, as the platform automates the deployment process. However, ensuring optimal resource allocation is essential for smooth operations.
Laurence Moseley - PeerSpot reviewer
Has a drag-and-drop interface and AI capabilities
It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data. Next, you can clean the data by handling missing values, selecting specific columns you want to analyze, and then proceeding with your analysis, such as regression or correlation. KNIME has over 4,500 nodes available for download. In addition, KNIME offers various extensions. For instance, the text processing extension allows you to process text data efficiently. It's more powerful than other tools like NVivo and Palantir. KNIME also has AI capabilities. If you're unsure about the next step, the AI assistant can suggest the most frequently used nodes based on your previous work. Another valuable feature is the integration with Python. If you need to perform a task that requires Python, you can simply add a Python node, write the necessary code,

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"The Cloudera Data Science Workbench is customizable and easy to use."
"We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics."
"The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine."
"The hardest part is keeping a tidy workspace because of the many nodes involved. When teaching, it would be helpful if there was more emphasis on how to group nodes effectively. For example, turning frequently used nodes into a single component can simplify things."
"It's a very powerful and simple tool to use."
"Valuable features include visual workflow creation, workflow variables (parameterisation), automatic caching of all intermediate data sets in the workflow, scheduling with the server."
"The ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database."
"We have found KNIME valuable when it comes to its visualization."
"Key features include: very easy-to-use visual interface; Help functions and clear explanations of the functionalities and the used algorithms; Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis."
 

Cons

"The tool's MLOps is not good. It's pricing also needs to improve."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
"It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data."
"There should be better documentation and the steps should be easier."
"Not just for KNIME, but generally for software and analyzing data, I would welcome facilities for analyzing different sorts of scale data like Likert scales, Thurstone scales, magnitude ratio scales, and Guttman scales, which I don't use myself."
"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"The ability to handle large amounts of data and performance in processing need to be improved."
"KNIME can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile."
 

Pricing and Cost Advice

"The product is expensive."
"KNIME is free and open source."
"KNIME is a cheap product. I currently use KNIME's open-source version."
"It's an open-source solution."
"It is free of cost. It is GNU licensed."
"This is a free open-source solution."
"The client versions are mostly free, and we pay only for the KNIME server version. It's not a cheap solution."
"While there are certain limitations in functionality, you can still utilize it efficiently free of charge."
"KNIME is an open-source tool, so it's free to use."
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Top Industries

By visitors reading reviews
Financial Services Firm
34%
Manufacturing Company
10%
Healthcare Company
9%
Computer Software Company
8%
Financial Services Firm
13%
Manufacturing Company
11%
Computer Software Company
9%
University
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Cloudera Data Science Workbench?
I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy...
What needs improvement with Cloudera Data Science Workbench?
The tool's MLOps is not good. It's pricing also needs to improve.
What is your primary use case for Cloudera Data Science Workbench?
We have different use cases. Our banking use case uses machine learning to identify customer life events and recommend the best-suited card products. These machine-learning models are deployed in o...
What do you like most about KNIME?
Since KNIME is a no-code platform, it is easy to work with.
What is your experience regarding pricing and costs for KNIME?
I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.
What needs improvement with KNIME?
For graphics, the interface is a little confusing. So, this is a point that could be improved.
 

Also Known As

CDSW
KNIME Analytics Platform
 

Overview

 

Sample Customers

IQVIA, Rush University Medical Center, Western Union
Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Find out what your peers are saying about Cloudera Data Science Workbench vs. KNIME and other solutions. Updated: March 2025.
845,040 professionals have used our research since 2012.