Google Cloud Dataflow vs Starburst Galaxy comparison

Google and Starburst are both solutions in the Streaming Analytics category. Google is ranked #9 with an average rating of 8.1, while Starburst is ranked #12 with an average rating of 9.7. Google holds a 5.1% mindshare in SA, compared to Starburst’s 1.3% mindshare. Additionally, 93% of Google users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Google Cloud Dataflow

Read 14 Google Cloud Dataflow reviews

2,451 Views
2,034 Comparison Views

93% willing to recommend

Starburst Galaxy

Read 9 Starburst Galaxy reviews

616 Views
437 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Starburst Galaxy

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Google Cloud Dataflow and Starburst Galaxy based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Google Cloud Dataflow vs. Starburst Galaxy Report (Updated: September 2025).

Buyer's Guide

Google Cloud Dataflow vs. Starburst Galaxy

September 2025

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Helped 869,202 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 Dataflow

Ranking in Streaming Analytics

9th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Starburst Galaxy

Ranking in Streaming Analytics

12th

Average Rating

9.8

Reviews Sentiment

1.0

Number of Reviews

Ranking in other categories

Data Science Platforms (9th)

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Google Cloud Dataflow is 5.1%, down from 7.8% compared to the previous year. The mindshare of Starburst Galaxy is 1.3%, down from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Google Cloud Dataflow	5.1%
Starburst Galaxy	1.3%
Other	93.6%

Streaming Analytics

Featured Reviews

Jana Polianskaja

Data Engineer at Accenture

Build Scalable Data Pipelines with Apache Beam and Google Cloud Dataflow

As a data engineer, I find several features of Google Cloud Dataflow particularly valuable. The ability to test solutions locally using Direct Runner is crucial for development, allowing me to validate pipelines without incurring the costs of full Dataflow jobs. The unified programming model for both batch and streaming processing is exceptional - requiring only minor code adjustments to optimize for either mode. This flexibility extends to language support, with robust implementations in both Java and Python, allowing teams to leverage their existing expertise. The platform's comprehensive monitoring capabilities are another standout feature. The intuitive interface, Grafana integration, and extensive service connectivity make troubleshooting and performance tracking highly efficient. Furthermore, seamless integration with Google Cloud Composer (managed Airflow) enables sophisticated orchestration of data pipelines.

Read full review

Stephen-Howard

Sr Director, Technology at Spreetail

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.

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

"The integration within Google Cloud Platform is very good."

"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."

"It is a scalable solution."

"The product's installation process is easy...The tool's maintenance part is somewhat easy."

"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."

"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."

"The solution allows us to program in any language we desire."

"The service is relatively cheap compared to other batch-processing engines."

More Google Cloud Dataflow pros

"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."

"Starburst has provided us with virtually guaranteed performance on complex queries across datasets that are in the tens of gigabytes which complete in seconds."

"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 has provided us with virtually guaranteed performance on complex queries across datasets that are in the tens of gigabytes which complete in seconds."

"Starburst Galaxy has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes."

"The most fundamental feature is the query engine, which is much faster than any of the competitors; Starburst is able to finish most queries within 10 seconds, which is especially important for many non-technical employees."

"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."

Cons

"Occasionally, dealing with a huge volume of data causes failure due to array size."

"Promoting the technology more broadly would help increase its adoption."

"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."

"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."

"The deployment time could also be reduced."

"The technical support has slight room for improvement."

"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."

"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."

More Google Cloud Dataflow cons

"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."

"Cluster startup time can be slow, sometimes taking over a minute."

"The most persistent issue is the cluster spin-up time."

"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."

Pricing and Cost Advice

"The solution is not very expensive."

"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."

"The tool is cheap."

"Google Cloud Dataflow is a cheap solution."

"Google Cloud is slightly cheaper than AWS."

"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."

"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."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Manufacturing Company

12%

Retailer

10%

Computer Software Company

Financial Services Firm

29%

Computer Software Company

14%

Government

Consumer Goods Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	10

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	1

Questions from the Community

What do you like most about Google Cloud Dataflow?

The product's installation process is easy...The tool's maintenance part is somewhat easy.

See all answers

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.

See all answers

What needs improvement with Google Cloud Dataflow?

It can be improved in several ways. The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability. Implementing AI-based suggest...

See all answers

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...

See all answers

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...

See all answers

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...

See all answers

Comparisons

Databricks vs Google Cloud Dataflow

Compared 34% of the time

Apache Flink vs Google Cloud Dataflow

Compared 22% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 13% of the time

Spring Cloud Data Flow vs Google Cloud Dataflow

Compared 7% of the time

Amazon MSK vs Google Cloud Dataflow

Compared 5% of the time

More Google Cloud Dataflow Competitors

Starburst Enterprise vs Starburst Galaxy

Compared 52% of the time

Databricks vs Starburst Galaxy

Compared 29% of the time

Dremio vs Starburst Galaxy

Compared 19% of the time

More Starburst Galaxy Competitors

Product Reports

Buyer's Guide

Google Cloud Dataflow

September 2025

Download Google Cloud Dataflow product report

Buyer's Guide

Starburst Galaxy

September 2025

Download Starburst Galaxy product report

Also Known As

Google Dataflow

No data available

Overview

Google Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Google

Starburst Galaxy offers advanced analytics solutions tailored for big data environments. It is renowned for its scalability and flexibility, enabling data-driven decisions.

Starburst Galaxy provides an efficient platform for conducting complex data analysis across multiple datasets. Its architecture facilitates seamless integration with existing data infrastructure, allowing organizations to harness the value of their data efficiently. Users appreciate its ability to perform intricate queries without the need for extensive data movement, fostering a more agile approach to data management and analysis.

What features make Starburst Galaxy unique?

Advanced Query Engine: Optimizes data processing to handle complex analytics efficiently.
Scalability: Easily adapts to growing data needs without compromising performance.
Integration Capabilities: Seamlessly connects with existing data systems for streamlined operations.

What benefits and ROI should users look for?

Cost Efficiency: Reduces storage and compute costs by optimizing queries and minimizing data movement.
Improved Decision-Making: Empowers users with quick access to critical insights for strategic planning.
Enhanced Performance: Delivers faster query responses, boosting productivity and time-to-value.

Starburst Galaxy is instrumental in industries such as finance, healthcare, and e-commerce, where large-scale data analysis is crucial. Organizations in these fields implement its features to accelerate data processing and gain a competitive advantage, turning vast amounts of information into actionable insights that drive industry-specific innovations.

Starburst

Sample Customers

Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale

Information Not Available

Buyer's Guide

Google Cloud Dataflow vs. Starburst Galaxy

September 2025

Free Report: Google Cloud Dataflow vs. Starburst Galaxy

Find out what your peers are saying about Google Cloud Dataflow vs. Starburst Galaxy and other solutions. Updated: September 2025.

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