Google Cloud Datalab vs SAS Enterprise Miner comparison

Google and SAS are both solutions in the Data Science Platforms category. Google is ranked #22, while SAS is ranked #21. Google holds a 1.7% mindshare in Data Science Platforms, compared to SAS’s 2.1% mindshare. Additionally, 80% of Google users are willing to recommend the solution, compared to 93% of SAS users who would recommend it.

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

2,400 Views
1,416 Comparison Views

80% willing to recommend

SAS Enterprise Miner

Read 13 SAS Enterprise Miner reviews

2,341 Views
1,849 Comparison Views

93% willing to recommend

Google Cloud Datalab

SAS Enterprise Miner

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

SAS Enterprise Miner and Google Cloud Datalab compete in the data analytics and machine learning space. Google Cloud Datalab has an advantage with its ease of use and scalability.

Features: SAS Enterprise Miner offers robust statistical modeling, decision trees, and regression capabilities, ideal for comprehensive analytics projects. Google Cloud Datalab provides seamless integration with Google’s ecosystem, solidifying its adaptability in data visualization, machine learning, and collaboration for cloud-based projects.

Room for Improvement: SAS Enterprise Miner could enhance integration with cloud services, improve user interface intuitiveness, and simplify deployment processes. Google Cloud Datalab may improve on its AI configurations, its cost structure for large-scale data applications, and documentation specificity for complex implementations.

Ease of Deployment and Customer Service: Google Cloud Datalab uses a straightforward cloud-based deployment model with extensive documentation and community support, allowing for quick setup. SAS Enterprise Miner demands more specialized expertise due to its on-premise deployment, requiring more setup resources.

Pricing and ROI: SAS Enterprise Miner involves higher setup costs due to infrastructure and licensing, promising solid ROI for long-term projects. Google Cloud Datalab’s cloud-based pricing model is more cost-effective, suitable for organizations seeking fast ROI through cloud integration.

To learn more, read our detailed Google Cloud Datalab vs. SAS Enterprise Miner Report (Updated: June 2026).

Buyer's Guide

Google Cloud Datalab vs. SAS Enterprise Miner

June 2026

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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

Ranking in other categories

Data Visualization (26th)

SAS Enterprise Miner

Ranking in Data Science Platforms

21st

Average Rating

7.6

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

Data Mining (7th)

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 SAS Enterprise Miner is 2.1%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
SAS Enterprise Miner	2.1%
Google Cloud Datalab	1.7%
Other	96.2%

Data Science Platforms

Featured Reviews

LijomonJose

System Architect at UST Global EspaÃ±a

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.

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reviewer1352853

Executive Head of analytics at a retailer with 5,001-10,000 employees

A stable product that is easy to deploy and can be used for structured and unstructured data mining

We use the solution for predictive analytics to do structured and unstructured data mining I like the way the product visually shows the data pipeline. The product must provide better integration with cloud-native technologies. I have been using the solution for 20 years. The product is very…

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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."

"Google Cloud Datalab is very customizable."

"The APIs are valuable."

"All of the features of this product are quite good."

"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 solution is able to handle quite large amounts of data beautifully."

"The setup is straightforward. Deployment doesn't take more than 30 minutes."

"Technical support has been good, and when I called them at the start of using the product with some issues they were very helpful."

"The data processing of the solution is very good, easy to use, both for enterprise and personal use."

"I found the ease of use of the solution the most valuable."

"Performance is excellent."

"SAS internal support is very qualified and if we have any issues, we contact them and trust that they can help."

"Good data management and analytics."

More SAS Enterprise Miner pros

Cons

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"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 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."

"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."

"Price of the product"

"The solution is quite expensive. The pricing is too high."

"Technical support could be improved."

"Virtualization could be much better."

"We really don't like the protocols the solution offers. The solution is much more complex than other options."

"The product must provide better integration with cloud-native technologies."

"The license is really expensive. This solution is for large corporations because not everybody can afford it."

"The initial setup is challenging if doing it for the first time."

More SAS Enterprise Miner cons

Pricing and Cost Advice

"It is affordable for us because we have a limited number of users."

"The product is cheap."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."

"This solution is for large corporations because not everybody can afford it."

"The solution must improve its licensing models."

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Top Industries

By visitors reading reviews

Construction Company

20%

Financial Services Firm

13%

Outsourcing Company

University

Financial Services Firm

19%

Construction Company

13%

Educational Organization

11%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	4
Large Enterprise	7

Comparisons

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Compared 7% of the time

Dataiku vs Google Cloud Datalab

Compared 7% of the time

Anaconda Business vs Google Cloud Datalab

Compared 6% of the time

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KNIME Business Hub vs SAS Enterprise Miner

Compared 9% of the time

IBM SPSS Modeler vs SAS Enterprise Miner

Compared 8% of the time

Azure Data Lake Analytics vs SAS Enterprise Miner

Compared 6% of the time

IBM SPSS Statistics vs SAS Enterprise Miner

Compared 6% of the time

Altair RapidMiner vs SAS Enterprise Miner

Compared 4% of the time

More SAS Enterprise Miner Competitors

Product Reports

Buyer's Guide

Google Cloud Datalab

August 2026

Download Google Cloud Datalab product report

Buyer's Guide

Data Mining

July 2026

Download SAS Enterprise Miner product report

Also Known As

No data available

Enterprise Miner

Overview

Google Cloud Datalab offers an integrated environment for seamless data processing and analysis. It combines robust infrastructure with free call-up features to enhance user experience, making it a go-to choice for data-driven tasks.

Google Cloud Datalab is geared towards users seeking efficient data handling solutions. It provides a seamless setup with robust infrastructure, focusing on enhancing APIs and offering meaningful data visualization through its dashboards. Notable AI capabilities include auto-completion and data logging, although some minor configuration challenges exist. While transitioning from AWS can be complex, the platform supports dynamic data pipeline design that suits Python development, offering an end-user friendly environment.

What are the key features of Google Cloud Datalab?

Robust Infrastructure: Provides seamless setup with strong backend support.
Free Call-Up: Allows use without extra costs, enhancing accessibility.
Data Visualization: Dashboards offer meaningful insights into data trends.
AI Capabilities: Features auto-completion and data logging for efficiency.
Customizable Architecture: Adapts to different data handling needs effectively.

What benefits should users expect from Google Cloud Datalab?

Efficient Data Analysis: Streamlines exploratory data analysis in machine learning.
Enhanced API Value: API integrations add significant value to data handling.
Friendly Python Environment: Suitable for end-users looking for ease in Python development.
Adaptive Pipeline Design: Supports complex data transitions from AWS.
Global Availability: Helps overcome time lag issues with server location selection.

In specific industries, Google Cloud Datalab is instrumental in managing data analysis, machine learning exploration, and dataset preprocessing. It facilitates the transfer of workloads from AWS and ensures efficient daily data processing. Organizations benefit from its capability to provision machine learning models into Vertex AI, bolstering research and development efforts. The global availability feature plays a significant role in selecting optimal server locations, addressing time lag and connectivity challenges.

Google

SAS Enterprise Miner enables comprehensive data management and analytics, handling extensive data volumes with diverse algorithms for model creation. Its integration and flexibility in SAS code usage make it suitable for both enterprise and personal use.

SAS Enterprise Miner is recognized for its data pipeline visualization, data processing, and statistical modeling capabilities. Its user-friendly GUI and automation support data mining tasks, decision tree creation, and clustering. However, improvements are needed in its interface visualization, affordability, technical support, and integration with languages like Python and cloud-native tech. Enhanced performance, visualization, and model development auditing, along with text analytics in the main license, are desirable upgrades. Integration with Microsoft SQL and combined offerings remains a priority.

What are SAS Enterprise Miner's most important features?

Data Management: Handles large data volumes efficiently.
Analytics: Integrates diverse algorithms for accurate model creation.
Integration: Supports flexible incorporation of SAS code.
Automation: Enhances data mining tasks through automation.
Clustering: Facilitates extensive clustering operations.

What benefits or ROI should users look for in reviews?

Performance: Delivers remarkable efficiency in data processing tasks.
Compatibility: Seamless integration with other technologies.
Flexibility: Adaptability for enterprise and personal use cases.
User-Friendly: Intuitive GUI aids in simplifying complex tasks.

SAS Enterprise Miner is applied across industries like banking, insurance, and healthcare for data mining, machine learning, and predictive analytics. It aids in activities such as text mining, fraud modeling, and forecasting model creation, handling structured and unstructured data, and performing ad hoc analysis to model business processes and analyze data clusters.

SAS

Sample Customers

Information Not Available

Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare

Buyer's Guide

Google Cloud Datalab vs. SAS Enterprise Miner

June 2026

Free Report: Google Cloud Datalab vs. SAS Enterprise Miner

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