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Google Cloud Dataflow vs Starburst Galaxy comparison

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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 Dataflow
Ranking in Streaming Analytics
12th
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
15
Ranking in other categories
No ranking in other categories
Starburst Galaxy
Ranking in Streaming Analytics
8th
Average Rating
9.4
Reviews Sentiment
2.6
Number of Reviews
12
Ranking in other categories
Data Science Platforms (7th)
 

Mindshare comparison

As of October 2026, in the Streaming Analytics category, the mindshare of Google Cloud Dataflow is 3.5%, down from 5.1% compared to the previous year. The mindshare of Starburst Galaxy is 1.7%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Starburst Galaxy1.7%
Google Cloud Dataflow3.5%
Other94.8%
Streaming Analytics
 

Featured Reviews

Mohammed Aaqibuddin - PeerSpot reviewer
Senior Cdp Specialist at DigitasLBi
Unified user personas have improved data workflows and support detailed monitoring and logging
Google Cloud has many streams and products. In Google Cloud, everything is translated in the backend, so we do not have to use services such as Apache Beam. When you want to use Google Cloud Functions, you write the code, and the backend talks to all the libraries or Apache, so we do not need to be concerned about those. We just need to use our functions that translate and have many tools and services readily available. Google Cloud Dataflow has made it very easy for detailed monitoring and logging features for pipeline performance assessment. For example, if I am using Google Cloud Functions, I can easily see what changes I have done and trace it properly. I can see what is happening with this script, how many users are affected, whether the script is working, what is failing, and how we can rectify issues with proper monitoring.
Pedromachado Ventura - PeerSpot reviewer
Data Analyst at a financial services firm with 5,001-10,000 employees
Unified SQL layer has streamlined access to distributed historical data for analytics and reporting
One area that I think could be improved is the experience when performance issues occur. When a query is slow, it is not always immediately obvious to me whether the bottleneck comes from Starburst Galaxy itself, the underlying data source, the query design, or the reporting tool. Better visibility into query performance and easier diagnostics for non-administrators would be useful. Another potential improvement would be enhancing the experience with BI tools to make it more seamless. I work a lot with Power BI, and when you are working with larger data sets, performance can sometimes depend on several different layers. Having more visibility into what is happening between the BI tool, Starburst Galaxy, and the underlying source would be helpful. I also think onboarding could be a little more accessible for analysts. There is good technical documentation, but sometimes I just need to understand the best way to approach a common use case without diving too deep into the platform architecture. The main improvement would be troubleshooting. I have not used the AI capabilities extensively, so I cannot give a detailed assessment. I am not sure if my organization has the full capabilities of Starburst Galaxy, but I think adding AI on top of the data layer is interesting, especially if it can help users discover data, understand data sets, and interact with them more naturally. For governance and security, one of the strengths of Starburst Galaxy is that you can centralize access to data while still controlling what different users are allowed to see. Role-based access, fine-grained permissions, and data masking are important because giving people easier access to data should not mean giving everyone access to everything. I think that is even more important than any AI capabilities that are introduced. If you do introduce AI, I think it should respect exactly the same data permissions and governance rules as the user that is accessing the data.

Quotes from Members

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

Pros

"It is a scalable solution."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%."
"The support team is good and it's easy to use."
"I would rate the overall solution a ten out of ten."
"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."
"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 Galaxy has positively impacted my organization by allowing us to rethink the strategy for data and architect data differently; instead of having multiple data marts and siloed data marts, we have a unified vision, and that is how it is changing."
"Starburst Galaxy is becoming a cornerstone of our data platform, empowering us to make smarter and faster decisions across the organization."
"I am now able to answer questions in a couple of minutes that would otherwise take hours or days of time for my data engineering teams."
"Starburst Galaxy has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes."
"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 has significantly improved our data architecture flexibility and performance management by solving cross-database query challenges and enabling us to utilize iceberg tables externally across our entire data ecosystem."
"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."
 

Cons

"There are certain challenges regarding the Google Cloud Composer which can be improved."
"The technical support has slight room for improvement."
"Promoting the technology more broadly would help increase its adoption."
"They should do a market survey and then make improvements."
"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."
"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."
"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."
"Compared to other support systems, such as those in Braze, Tealium, Google, and others like Adobe, Google Cloud takes more time because it is a bigger company."
"Cluster startup time can be slow, sometimes taking over a minute."
"I would like Starburst to leverage AI to improve usability. Data lakes are complicated and difficult for users to explore."
"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."
"As a hosted option, I wish I had more control over the cluster configuration, specifically regarding some of the more advanced options."
"Multi-tenancy could be improved. In order to have multiple environments for SSO, we maintain multiple tenants that are connected to different AWS accounts via the Marketplace."
"The most persistent issue is the cluster spin-up time."
"There is still room for improved usability and diagnostics, especially for users who are not platform specialists."
"Starburst Galaxy can be improved by discovering unstructured data and building in streaming ingestion because we are currently using Kafka for that purpose."
 

Pricing and Cost Advice

"The tool is cheap."
"Google Cloud Dataflow is a cheap solution."
"The solution is not very expensive."
"Google Cloud is slightly cheaper than AWS."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."
"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."
"The solution is cost-effective."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Manufacturing Company
9%
Comms Service Provider
7%
Construction Company
6%
Financial Services Firm
30%
Computer Software Company
10%
Manufacturing Company
6%
Construction Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise12
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise2
Large Enterprise4
 

Questions from the Community

What is your experience regarding pricing and costs for Google Cloud Dataflow?
Pricing is normal. It is part of a package received from Google, and they are not charging us too high.
What needs improvement with Google Cloud Dataflow?
I feel there could be something that they can introduce, such as when we have data in the tables, a feature that creates a unique persona of the user automatically, so we do not have to do that man...
What is your primary use case for Google Cloud Dataflow?
The primary use case for Google Cloud Dataflow is when a brand has a lot of data and wants to store it in their warehouse. They can use BigQuery to store their data or use big data solutions to sto...
What is your experience regarding pricing and costs for Starburst Galaxy?
I recommend experimenting with different cluster sizes to determine what works best for your particular use case.
What needs improvement with Starburst Galaxy?
One area that I think could be improved is the experience when performance issues occur. When a query is slow, it is not always immediately obvious to me whether the bottleneck comes from Starburst...
What is your primary use case for Starburst Galaxy?
I have mainly used Starburst Galaxy as a query and access layer for analytical data, as my current work involves dealing with data stored across different platforms, including Hive and SingleStore....
 

Also Known As

Google Dataflow
No data available
 

Overview

 

Sample Customers

Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Information Not Available
Find out what your peers are saying about Google Cloud Dataflow vs. Starburst Galaxy and other solutions. Updated: September 2026.
917,403 professionals have used our research since 2012.