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Alteryx vs Google Cloud Datalab 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

Alteryx
Ranking in Data Science Platforms
6th
Average Rating
8.4
Reviews Sentiment
7.0
Number of Reviews
84
Ranking in other categories
Predictive Analytics (1st), Data Preparation Tools (1st)
Google Cloud Datalab
Ranking in Data Science Platforms
19th
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
6
Ranking in other categories
Data Visualization (17th)
 

Mindshare comparison

As of February 2026, in the Data Science Platforms category, the mindshare of Alteryx is 3.8%, down from 6.5% compared to the previous year. The mindshare of Google Cloud Datalab is 1.5%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Alteryx3.8%
Google Cloud Datalab1.5%
Other94.7%
Data Science Platforms
 

Featured Reviews

Rajneesh Prajapati - PeerSpot reviewer
Senior Rpa Consultant at Accely Consulting
Time-saving workflows have transformed data preparation and predictive analysis for my team
Some of the best features Alteryx offers are its no and low-code capabilities. It delivers massive time-saving and includes spatial and predictive analysis. Alteryx includes built-in tools such as drive time analysis and linear regression, which are much harder to achieve in standard BI tools such as Power BI or Tableau. In addition to these features, Alteryx provides built-in spatial tools that can calculate drive time and location-based insights with minimal effort through drag-and-drop spatial tools, low complex coding, faster, and more accurate results. Linear regression predicts sales based on marketing spend, estimates costs based on usage, and identifies trends in historical data. Alteryx has positively impacted my organization by saving time, improving accuracy, and enabling better decision-making. Using Alteryx, complex tasks such as data cleansing, joining datasets, drive time analysis, and linear regression can be done much faster compared to manual Excel or SQL work. This reduces dependency on manual effort and lowers the risk of human error. Drive time analysis helps my organization make better location-based decisions, such as identifying optimal service areas or improving customer reach.
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.

Quotes from Members

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

Pros

"The tools are built-in. You just plug and play, drag and drop, once you understand how to use the tools, it is easy."
"Data processing is most valuable. It is one of the fastest data blockers out there in the market, which is a fascinating thing about Alteryx."
"The three data signs and data engineering are great features."
"The feature that I have found most valuable for Alteryx is its geo-referencing feature, it is very good. I use it a lot, especially for supply chain."
"The philosophy of the citizen data scientist is the key piece, which means the no-code analytics capability. This is the feature that attracted us the most."
"The connectors are a very good feature."
"All of the data science features in terms of unioning and joining data together are valuable."
"The GUI is simple and it integrates with Python."
"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."
"For me, it has been a stable product."
"Google Cloud Datalab is very customizable."
"All of the features of this product are quite good."
"The APIs are valuable."
 

Cons

"We can't browse multiple files. When we deploy a solution on a gallery, let's say I have ten different files, and I have to upload them all at once. This is something that's difficult in the gallery. So case by case, I see some downsides, but often we do something alternative."
"The product could benefit from improved integration with visualization tools or even the inclusion of built-in visualization features."
"Licensing negotiations were problematic, affecting our product usage."
"There were times when the product would fail during development without an apparent reason."
"We are hoping that the NLP features will also support Chinese characters."
"The only weakness I would say is on the visualization side of having that dynamic incapability."
"When a process completes there is a notification, but the notification does not include the process's name."
"Pricing flexibility can be better, especially for small teams and organizations, as the current licensing cost can be a barrier for wider adoption."
"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."
"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."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The interface should be more user-friendly."
 

Pricing and Cost Advice

"A designer and scheduler for $13K/year in total is pretty much earning you the money back in time and in other resources."
"The desktop platform costs $5,000 per year. It's very costly."
"The designer has a list price of $5,995 USD."
"​Very transparent.​"
"The pricing is $5000 per year per production license."
"I rate the solution's pricing as a ten, as it is highly priced."
"We use the free version of the solution. There are enterprise licenses available. It cost approximately $5,000 annually. It is an expensive solution and there are additional features that cost more money."
"There are some implementation services and internal effort costs at the beginning but there is nothing else."
"The product is cheap."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Manufacturing Company
9%
Computer Software Company
8%
Healthcare Company
5%
Financial Services Firm
24%
Computer Software Company
10%
University
9%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business32
Midsize Enterprise15
Large Enterprise53
No data available
 

Questions from the Community

What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?
One of the differences is that with Alteryx you can use it as an ETL and analytics tool. Please connect with me directly if you want to know more.
What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?
Alteryx is an extremely easy and flexible data tool, flexible in terms of drag and drop toolset and also has python, R integrations if your team requires this. It can handle over 2 billion rows of...
What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?
I am not familiar with IBM SPSS Modeler, therefore, I cannot compare these two products. Regarding Alteryx I can say the following: - An excellent desktop tool for Data Prep and analytics. - Featu...
What do you like most about Google Cloud Datalab?
Google Cloud Datalab is very customizable.
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 ...
 

Overview

 

Sample Customers

AnalyticsIq Inc., belk, BloominBrands Inc., Cardinalhealth, Cineplex, Dairy Queen
Information Not Available
Find out what your peers are saying about Alteryx vs. Google Cloud Datalab and other solutions. Updated: December 2025.
881,455 professionals have used our research since 2012.