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Google Cloud Datalab vs KNIME Business Hub comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Jun 3, 2026

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

Google Cloud Datalab
Ranking in Data Science Platforms
22nd
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
6
Ranking in other categories
Data Visualization (26th)
KNIME Business Hub
Ranking in Data Science Platforms
3rd
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
66
Ranking in other categories
Data Mining (1st)
 

Mindshare comparison

As of September 2026, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.7%, up from 1.1% compared to the previous year. The mindshare of KNIME Business Hub is 4.2%, down from 12.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
KNIME Business Hub4.2%
Google Cloud Datalab1.7%
Other94.1%
Data Science Platforms
 

Featured Reviews

LJ
System Architect at UST Global España
dashboards are good and data visualization is more meaningful for the end-user
Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.
NataliaRaffo - PeerSpot reviewer
Co Founder & Chief Data Officer Cdo at NTT DATA
Workflow automation has accelerated advanced analytics and machine learning delivery
Sometimes it is a little bit difficult to use some nodes when we have many large-scale data, for example, CSV files with a large amount of data. It is sometimes difficult to try to import the data in KNIME Business Hub nodes because I think that some features that are in the CSV in text, for example, large text, is difficult for KNIME Business Hub to import these fields. I don't know why, but it is very difficult. We need to try to use different nodes for importing the data, such as File Reader and CSV Reader. However, I think that it is always the features that have much text, it is difficult for KNIME Business Hub to understand and import this information. I don't know why, or maybe I don't know if we don't know what the better option is to configure the node to import all the CSV or the data set. However, we have always had this problem. In some nodes, sometimes it is the same because sometimes, for example, I have a CSV and in my CSV, I have a feature that is, for example, a date. When I import this data set in the File Reader node, I have problems with this field because it is a date, but the problem is that it imports it as text, for example. We try to use their nodes that convert text to date, but sometimes it is difficult, and it is not immediate to transform the text into a date. So we needed to convert the text into a date in the CSV, and then import it again in the KNIME Business Hub node and try to have a good read of this field. I know that KNIME Business Hub has some nodes to convert text to date and others, but sometimes it is difficult to use these nodes. I don't know why. Maybe it needs a specific format for the date and we need to transform our feature in this option. So sometimes it is a large process to convert these features. However, sometimes we need to investigate and search for other nodes, and try with other nodes to import these cases.

Quotes from Members

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

Pros

"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"The APIs are valuable."
"Google Cloud Datalab is very customizable."
"For me, it has been a stable product."
"All of the features of this product are quite good."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"I use it personally for my purposes and for the company; I use it for internal data science with very good results."
"We have found KNIME valuable when it comes to its visualization."
"It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea."
"We are able to automate several functions which were done manually, and I can integrate several data sets quickly and easily to support analytics."
"It is a free open-source tool that performs very similarly to other expensive tools."
"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 interface should be more user-friendly."
"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"The product must be made more user-friendly."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."
"I think the data visualization part is the area most in need of improvement."
"There should be better documentation and the steps should be easier."
"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."
"Compared to the other data tools on the market, the user interface can be improved."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"Other solutions should be considered for enterprise level implementation."
"I feel the query performance is slower than my old code."
 

Pricing and Cost Advice

"The pricing is quite reasonable, and I would give it a rating of four out of ten."
"It is affordable for us because we have a limited number of users."
"The product is cheap."
"KNIME offers a free version"
"KNIME assets are stand alone, as the solution is open source."
"The price for Knime is okay."
"KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website."
"There is no cost for using KNIME because it is an open-source solution, but you have to pay if you need a server."
"I use the open-source version."
"The client versions are mostly free, and we pay only for the KNIME server version. It's not a cheap solution."
"This is an open-source solution that is free to use."
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Top Industries

By visitors reading reviews
Construction Company
18%
Comms Service Provider
12%
Financial Services Firm
11%
Outsourcing Company
10%
Financial Services Firm
11%
Manufacturing Company
8%
University
8%
Outsourcing Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business22
Midsize Enterprise16
Large Enterprise35
 

Questions from the Community

Ask a question
Earn 20 points
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?
KNIME Business Hub could be improved by making the user experience more intuitive, especially for users who are new to data analytics. More guided features and documentation would be beneficial.
What is your primary use case for KNIME?
I automated the processing of paper-based data by digitalizing scanned documents and creating a KNIME Business Hub workflow that transforms the data into an analysis-ready format, reducing a manual...
 

Also Known As

No data available
KNIME Analytics Platform
 

Overview

 

Sample Customers

Information Not Available
Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Find out what your peers are saying about Google Cloud Datalab vs. KNIME Business Hub and other solutions. Updated: September 2026.
913,806 professionals have used our research since 2012.