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Google Cloud Datalab vs KNIME Business Hub comparison

 

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
5th
Average Rating
8.2
Reviews Sentiment
6.8
Number of Reviews
63
Ranking in other categories
Data Mining (2nd)
 

Mindshare comparison

As of August 2026, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.7%, up from 1.0% compared to the previous year. The mindshare of KNIME Business Hub is 4.5%, down from 12.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
KNIME Business Hub4.5%
Google Cloud Datalab1.7%
Other93.8%
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

"For me, it has been a stable product."
"Google Cloud Datalab is very customizable."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The APIs are valuable."
"All of the features of this product are quite good."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders."
"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."
"Data preparation and data modeling are easy to do."
"This is a very handy tool and I use it quite interactively."
"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."
"We are able to automate several functions which were done manually, and I can integrate several data sets quickly and easily to support analytics."
"The tool's analytic capabilities are good."
"It's a huge tool with machine learning features as well."
 

Cons

"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."
"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."
"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 interface should be more user-friendly."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"KNIME is not scalable."
"I'd like something that would make it easier to connect/parse websites, although I will fully admit that I'm not as proficient in KNIME as I would like to be, so it could be I'm just missing something."
"Other solutions should be considered for enterprise level implementation."
"To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages."
"Scalability is limited to a desktop application."
"KNIME's documentation is not strong."
"There should be better documentation and the steps should be easier."
"KNIME could improve when it comes to large data markets."
 

Pricing and Cost Advice

"It is affordable for us because we have a limited number of users."
"The pricing is quite reasonable, and I would give it a rating of four out of ten."
"The product is cheap."
"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."
"It's an open-source solution."
"The price for Knime is okay."
"They have different versions, but I am using the open-source one."
"It is free of cost. It is GNU licensed."
"Scaling to the on-premises version requires a licensing fee per user that is a bit expensive in comparison to R, Python, and SAS."
"KNIME assets are stand alone, as the solution is open source."
"KNIME is a cost-effective solution because it’s free of cost."
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Top Industries

By visitors reading reviews
Construction Company
20%
Financial Services Firm
13%
Outsourcing Company
9%
University
7%
Financial Services Firm
11%
University
8%
Manufacturing Company
8%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business21
Midsize Enterprise16
Large Enterprise32
 

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?
I would describe KNIME Decision Hub as somewhat helpful in making data-driven decisions more efficient. It could have been a scalable decisioning as a service at the back end, but it's not working ...
What is your primary use case for KNIME?
I mainly use KNIME Business Hub currently for data ETLs and then it meets with predictive analytics. Sometimes I utilize it for forecasting, but mostly it's predictive analytics. I have utilized bo...
 

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: June 2026.
908,877 professionals have used our research since 2012.