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Google Cloud Datalab vs Starburst Galaxy comparison

 

Comparison Buyer's Guide

Executive Summary

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
18th
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
6
Ranking in other categories
Data Visualization (19th)
Starburst Galaxy
Ranking in Data Science Platforms
13th
Average Rating
9.8
Reviews Sentiment
1.0
Number of Reviews
7
Ranking in other categories
Streaming Analytics (17th)
 

Mindshare comparison

As of August 2025, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.0%, up from 1.0% compared to the previous year. The mindshare of Starburst Galaxy is 0.8%, up from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Nilesh Gode - PeerSpot reviewer
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.
Stephen-Howard - PeerSpot reviewer
Federated querying delivers integrated data at record speed and reduces processing time
The biggest win has been the ability to combine data from multiple sources and deliver it to the business at record speed. This capability has allowed us to query directly through Starburst Galaxy, enabling teams to access integrated data that would otherwise be hard to pull together. This has reduced both our ETL processing time and storage costs. We are answering questions that would have been hard, if not impossible, to answer previously because the data came from disparate, disconnected sources.

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."
"For me, it has been a stable product."
"The APIs are valuable."
"All of the features of this product are quite good."
"Starburst Galaxy has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes."
"Starburst Galaxy serves as our primary SQL-based data processing engine, a strategic decision driven by its seamless integration with our AWS cloud infrastructure and its ability to deliver high performance with low-latency responses."
"Starburst on Trino, combined with our SQL-native data transformation tool SQLMesh, has delivered anywhere from a two to five times improvement in compute performance across our transformation DAG."
"Starburst Galaxy is becoming a cornerstone of our data platform, empowering us to make smarter and faster decisions across the organization."
"Starburst Galaxy has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes."
 

Cons

"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."
"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."
"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 product must be made more user-friendly."
"Cluster startup time can be slow, sometimes taking over a minute."
"Cluster startup time is another pain point, typically 3 to 5 minutes, which is not the worst with proper planning but can be annoying for ad-hoc work."
"The most persistent issue is the cluster spin-up time."
 

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."
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Top Industries

By visitors reading reviews
Financial Services Firm
30%
University
7%
Government
7%
Computer Software Company
6%
Financial Services Firm
32%
Computer Software Company
15%
University
7%
Government
6%
 

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.
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 is your experience regarding pricing and costs for Starburst Galaxy?
You pay for cluster uptime. It is important to be aggressive about autoscaling, as a single worker will get you a long way. I recommend never connecting a BI tool to your Galaxy cluster. Instead, w...
What needs improvement with Starburst Galaxy?
As a hosted option, I wish I had more control over the cluster configuration, specifically regarding some of the more advanced options. Trino is extremely flexible and powerful, but some of this fu...
What is your primary use case for Starburst Galaxy?
I use Starburst as a cost-efficient hosted option for Trino for data integration and ad-hoc analysis across a broad range of data sources. It is surprisingly useful to query SQL Server, a Google Sh...
 

Overview

Find out what your peers are saying about Google Cloud Datalab vs. Starburst Galaxy and other solutions. Updated: August 2025.
865,164 professionals have used our research since 2012.