Google Cloud Datalab vs TIBCO Data Science comparison

Google and TIBCO are both solutions in the Data Science Platforms category. Google is ranked #16 with an average rating of 7.8, while TIBCO is ranked #25. Google holds a 1.0% mindshare in Data Science Platforms, compared to TIBCO’s 0.6% mindshare. Additionally, 80% of Google users are willing to recommend the solution, compared to 100% of TIBCO users who would recommend it.

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

711 Views
612 Comparison Views

80% willing to recommend

TIBCO Data Science

Read 4 TIBCO Data Science reviews

335 Views
255 Comparison Views

100% willing to recommend

Google Cloud Datalab

TIBCO Data Science

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Google Cloud Datalab and TIBCO Data Science are cloud-based data science and machine learning solutions. Google Cloud Datalab excels in ease of use and integration within the Google Cloud ecosystem, while TIBCO Data Science stands out for its advanced analytics capabilities and enterprise integration.

Features: Google Cloud Datalab offers seamless integration with Google Cloud, Jupyter Notebook support, and easy collaboration which benefit users needing straightforward deployment in cloud analytics. TIBCO Data Science showcases sophisticated data manipulation, rich predictive analytics, and robust security measures, thriving in complex model management across varied environments.

Room for Improvement: Google Cloud Datalab could enhance its data visualization capabilities and address limitations in AI configuration that may require server restarts. Increasing support for non-Google Cloud environments may also be beneficial. TIBCO Data Science can improve by offering more competitive pricing structures and simplifying the initial setup process. Enhancing user-friendly interfaces for less technical users could further improve the product.

Ease of Deployment and Customer Service: Google Cloud Datalab provides streamlined deployment via Google Cloud integration, offering simplified setup and efficient resource management. TIBCO Data Science supports on-premises and hybrid deployments, favorable for organizations with intricate infrastructure needs, providing flexibility in various deployment scenarios.

Pricing and ROI: Google Cloud Datalab is known for competitive pricing appealing to budget-conscious tech buyers focused on cloud services, with a favorable cost-to-value ratio. TIBCO Data Science involves higher setup costs justified by its advanced features and tailored enterprise solutions. While the entry cost is higher, TIBCO's ROI can be substantial due to its comprehensive features.

To learn more, read our detailed Google Cloud Datalab vs. TIBCO Data Science Report (Updated: April 2025).

Buyer's Guide

Google Cloud Datalab vs. TIBCO Data Science

April 2025

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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

16th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (18th)

TIBCO Data Science

Ranking in Data Science Platforms

25th

Average Rating

7.6

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.0%, down from 1.2% compared to the previous year. The mindshare of TIBCO Data Science is 0.6%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Nilesh Gode

Project Manager - Data Science at Mettler-Toledo International Inc.

Easy to setup, stable and easy to design data pipelines

The scalability is average. We have not faced any issues with scalability. There are more than 500 end users using this solution in our company. It is an integral part of the daily operations. The usage pattern is not a one-time thing; employees regularly access and utilize the application. We use it at a global level with a scattered user base. This means that users don't all use the application at the same time. So, around 300 out of 500 employees use the solution, and this usage is spread out throughout the day.

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Veeresh-SHRINGARI

Portfolio Manager, AI & ML at a healthcare company with 10,001+ employees

A straightforward initial setup and good reporting but needs better documentation

It would be ideal if it could be put onto the NMP where you can make more of an analysis. Right now, people don't have enough time to go through the report and make an analysis. It should provide the information of what is on the report into some kind of a dialogue form. Then, a person can ask certain questions and it could interactively give the required report. In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues. If you are doing certain operations of RDBMS, you suffer in terms of the latency of the data. That can be improved upon. Users should be able to cross tables of the web pages they are developing on Spotfire and this needs to be really easy and convenient. Right now, you need to do a lot of tweaks. The solution should be more user-friendly and require fewer tweaks, extensions, and workarounds.

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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

"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."

"The APIs are valuable."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"Google Cloud Datalab is very customizable."

"The most valuable feature is the performance."

"The most valuable feature is the ease of setting up visualizations."

"We like the way we can drill down into each report to get back data on each project. From the portfolio level, I can see what is happening on it. That is a really important feature. I can look at indirect costs, for example, which are hitting each CIO portfolio. It's good to be able to see actual resources in terms of time as well as cost."

"The idea that you don't have to generate reports each day but they are sent automatically is great."

Cons

"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 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."

"The product must be made 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 scripting for customization could be improved."

"Additional templates would help to get things moving more quickly in terms of getting the reports out."

"I would like the visualization for the map of countries to be more easily configurable."

"In terms of performance, I can see there are some issues when you are working with big data. When we are taking it from the Data Lake, we have a lot of issues."

Pricing and Cost Advice

"The product is cheap."

"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."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

20%

University

12%

Computer Software Company

10%

Manufacturing Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Google Cloud Datalab?

Google Cloud Datalab is very customizable.

See all answers

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...

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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 ...

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Comparisons

IBM SPSS Statistics vs Google Cloud Datalab

Compared 45% of the time

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Compared 31% of the time

Cloudera Data Science Workbench vs Google Cloud Datalab

Compared 24% of the time

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Databricks vs TIBCO Data Science

Compared 38% of the time

Amazon SageMaker vs TIBCO Data Science

Compared 35% of the time

Alteryx vs TIBCO Data Science

Compared 28% of the time

More TIBCO Data Science Competitors

Product Reports

Buyer's Guide

Google Cloud Datalab

April 2025

Download Google Cloud Datalab product report

Buyer's Guide

Data Science Platforms

March 2025

Download TIBCO Data Science product report

Also Known As

No data available

Alpine Data Chorus

Overview

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

Google

TIBCO Spotfire Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.

TIBCO

Sample Customers

Information Not Available

Havas Media, Tipping Point Community, eviCore

Buyer's Guide

Google Cloud Datalab vs. TIBCO Data Science

April 2025

Free Report: Google Cloud Datalab vs. TIBCO Data Science

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