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Google Cloud Datalab vs KNIME Business Hub comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Feb 8, 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
16th
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
6
Ranking in other categories
Data Visualization (15th)
KNIME Business Hub
Ranking in Data Science Platforms
3rd
Average Rating
8.2
Reviews Sentiment
6.8
Number of Reviews
63
Ranking in other categories
Data Mining (1st)
 

Mindshare comparison

As of May 2026, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.8%, up from 0.9% compared to the previous year. The mindshare of KNIME Business Hub is 5.6%, down from 11.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
KNIME Business Hub5.6%
Google Cloud Datalab1.8%
Other92.6%
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

"All of the features of this product are quite good."
"Google Cloud Datalab is very customizable."
"For me, it has been a stable product."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"The APIs are valuable."
"All of the features of this product are quite good."
"This is a very handy tool and I use it quite interactively."
"It is a complete data science platform and especially good at scaling up data preparation and wrapping."
"It has allowed us to easily implement advanced analytics into various processes."
"It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured."
"It offers a node-based data integration and processing system connected through a user-friendly drag-and-drop interface. This makes it an excellent choice for data analytics and engineering tasks."
"The data analytics capabilities in KNIME are excellent; it's not just a statistical ETL tool, as we can go deeper and do various types of tasks beyond straight analytics."
"Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
 

Cons

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"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."
"The product must be made more user-friendly."
"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."
"The interface should be more user-friendly."
"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."
"Compared to the other data tools on the market, the user interface can be improved."
"One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well."
"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."
"The license is quite expensive for us."
"The pricing needs improvement."
"​The data visualization part is the area most in need of improvement."
"I feel the query performance is slower than my old code."
"The user interface could be a little bit more comfortable."
 

Pricing and Cost Advice

"The pricing is quite reasonable, and I would give it a rating of four out of ten."
"The product is cheap."
"It is affordable for us because we have a limited number of users."
"I use the open-source version."
"KNIME is free and open source."
"KNIME is a cost-effective solution because it’s free of cost."
"For beginners, the free desktop version is very attractive, but the full server version can be more expensive. I have only used the free version and it offers a fair pricing system. I have been promoting it to others without any compensation or request from the company, simply because I am enthusiastic about it. I am not aware of the pricing for the server version, but it seems to be widely used."
"There is a Community Edition and paid versions available."
"We're using the free academic license just locally. I went for KNIME because they have a free academic license."
"They have different versions, but I am using the open-source one."
"KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required."
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Construction Company
16%
Comms Service Provider
6%
Computer Software Company
6%
Financial Services Firm
12%
Manufacturing Company
9%
University
8%
Educational Organization
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business21
Midsize Enterprise16
Large Enterprise31
 

Questions from the Community

What needs improvement with Google Cloud Datalab?
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 forcin...
What is your primary use case for Google Cloud Datalab?
It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...
What advice do you have for others considering Google Cloud Datalab?
Overall, I would rate it a nine out of ten. Google Cloud is very good. Once you go through the features of Google Cloud, it's a good idea to get a GCP certification so you have an idea of how it ca...
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?
In my previous PeerSpot review from March 2024, I mentioned that KNIME was not very strong in visualization and that I wanted to see NLQ (Natural Language Query) and automated visualization capabil...
What is your primary use case for KNIME?
I mainly use KNIME for ETL and data integration projects, followed by clustering and customer segmentation, process mining, AI and machine learning preprocessing pipelines, and recently GenAI orche...
 

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: April 2026.
893,244 professionals have used our research since 2012.