Google Cloud Datalab vs Starburst Enterprise comparison

Google and Starburst are both solutions in the Data Science Platforms category. Google is ranked #16 with an average rating of 7.8, while Starburst is ranked #14 with an average rating of 8.5. Google holds a 1.0% mindshare in Data Science Platforms, compared to Starburst’s 2.2% mindshare. Additionally, 80% of Google users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

711 Views
612 Comparison Views

80% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

1,176 Views
1,011 Comparison Views

100% willing to recommend

Google Cloud Datalab

Starburst Enterprise

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

Google Cloud Datalab and Starburst Enterprise are competing in the data analytics domain. Google Cloud Datalab impresses with its integration in Google Cloud services, while Starburst Enterprise stands out due to its powerful features and deployment options for complex analytics needs.

Features: Google Cloud Datalab offers integration with Google Cloud, focusing on machine learning and collaborative analysis. Starburst Enterprise provides the ability to query data across diverse sources using Presto, with robust performance in data-intensive environments. The main differences are in data source versatility and machine learning capabilities.

Ease of Deployment and Customer Service: Google Cloud Datalab has a seamless setup within Google Cloud, supported by documentation and integrated support tools. Starburst Enterprise provides flexible deployment options, including on-premises, cloud, and hybrid solutions, with dedicated customer service. The key distinction is the broader deployment range and personalized support of Starburst Enterprise.

Pricing and ROI: Google Cloud Datalab typically offers a lower setup cost, appealing to users invested in Google Cloud, with ROI through optimized costs. Starburst Enterprise may have higher initial costs but delivers ROI through its efficiency in complex queries and compatibility with existing infrastructures. The differentiation lies in initial investment versus long-term value.

To learn more, read our detailed Google Cloud Datalab vs. Starburst Enterprise Report (Updated: April 2025).

Buyer's Guide

Google Cloud Datalab vs. Starburst Enterprise

April 2025

Download the complete report

Helped 850,028 peers since 2012

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)

Starburst Enterprise

Ranking in Data Science Platforms

14th

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Streaming Analytics (12th)

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 Starburst Enterprise is 2.2%, up from 1.6% 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.

Read full review

KamleshPant

Senior Software Architect at USEReady

Connects to any data source from any region and offers unified access

There are no specific projects supported by Starburst regarding AI initiatives or machine learning projects. In the future, if we have all the data available, we can definitely capitalize on AI/ML and LLM capabilities to summarize data and gain insights. That's our future goal, but we haven't reached that point yet. There should be support for REST API data sources to access data from the web. We often have data coming in and communicate with data sources via REST API calls. I don't see that capability in Starburst currently; everything is through JDBC or ODBC. If Starburst could seamlessly access data using REST API capabilities, it would be a game-changer. The self-service data management features, like self-service materialized views, are great, but they can be a bit complex for basic users to understand.

Read full review

Quotes from Members

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

Pros

"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."

"Google Cloud Datalab is very customizable."

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

"For me, it has been a stable product."

"The APIs are valuable."

"It's very scalable, fast performing, and supports many catalogs."

"We have noticed improvements in performance using Starburst Enterprise. It handles complex data, including reading and partitioning files. We can add a new catalog to Starburst Enterprise by providing connection details and service account information. This allows us to integrate with existing tools, such as the Snowflake database, which we use for data protection in our project."

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 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."

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

"There should be support for REST API data sources to access data from the web."

"Starburst Enterprise could improve by offering additional features similar to those provided by other SQL query tools. For example, incorporating functionalities like pivot tables would make it more feasible to use."

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."

"I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of using Starburst Enterprise can vary based on the amount of data you're processing and the type of machines you opt for, whether on AWS or another cloud platform."

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

850,028 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

20%

University

12%

Computer Software Company

10%

Manufacturing Company

Financial Services Firm

45%

Computer Software Company

Energy/Utilities Company

Government

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...

See all answers

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 ...

See all answers

What is your experience regarding pricing and costs for Starburst Enterprise?

I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of us...

See all answers

What needs improvement with Starburst Enterprise?

See all answers

What is your primary use case for Starburst Enterprise?

We use Starburst with one client who is exploring their ecosystem to remove data silos and enable data access across departments. It's a very big ecosystem, like a finance institute. They are curre...

See all answers

Comparisons

IBM SPSS Statistics vs Google Cloud Datalab

Compared 45% of the time

Databricks vs Google Cloud Datalab

Compared 31% of the time

Cloudera Data Science Workbench vs Google Cloud Datalab

Compared 24% of the time

More Google Cloud Datalab Competitors

Databricks vs Starburst Enterprise

Compared 43% of the time

Starburst Galaxy vs Starburst Enterprise

Compared 17% of the time

Dremio vs Starburst Enterprise

Compared 14% of the time

Alteryx vs Starburst Enterprise

Compared 9% of the time

Apache Spark Streaming vs Starburst Enterprise

Compared 4% of the time

More Starburst Enterprise Competitors

Product Reports

Buyer's Guide

Google Cloud Datalab

April 2025

Download Google Cloud Datalab product report

Buyer's Guide

Data Science Platforms

May 2025

Download Starburst Enterprise product report

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

Starburst Enterprise is a data analytics platform that enables organizations to access and analyze data from multiple sources, including cloud-based and on-premises data warehouses. It provides a single access point to all data sources, allowing users to query and analyze data without moving it between systems.

By providing a unified view, Starburst Enterprise helps organizations make better-informed decisions and improve operational efficiency, leading to better customer insights and more accurate forecasting. Overall, Starburst Enterprise is a powerful tool for organizations looking to unlock the full potential of their data.

Starburst

Buyer's Guide

Google Cloud Datalab vs. Starburst Enterprise

April 2025

Free Report: Google Cloud Datalab vs. Starburst Enterprise

Find out what your peers are saying about Google Cloud Datalab vs. Starburst Enterprise and other solutions. Updated: April 2025.

DOWNLOAD NOW

850,028 professionals have used our research since 2012.

See our Google Cloud Datalab vs. Starburst Enterprise report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.