Google Cloud Dataflow vs Starburst Galaxy comparison

Why PeerSpot?

Google and Starburst are both solutions in the Streaming Analytics category. Google is ranked #13 with an average rating of 8.4, while Starburst is ranked #9 with an average rating of 9.5. Google holds a 3.4% mindshare in SA, compared to Starburst’s 1.7% 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 15 Google Cloud Dataflow reviews

3,125 Views
2,688 Comparison Views

93% willing to recommend

Starburst Galaxy

Read 11 Starburst Galaxy reviews

2,101 Views
966 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Starburst Galaxy

Comparison Buyer's Guide

Download the report

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 2026).

Buyer's Guide

Google Cloud Dataflow vs. Starburst Galaxy

September 2026

Download the complete report

Helped 913,683 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

13th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Starburst Galaxy

Ranking in Streaming Analytics

9th

Average Rating

9.4

Reviews Sentiment

2.5

Number of Reviews

Ranking in other categories

Data Science Platforms (7th)

Mindshare comparison

As of September 2026, in the Streaming Analytics category, the mindshare of Google Cloud Dataflow is 3.4%, down from 5.4% compared to the previous year. The mindshare of Starburst Galaxy is 1.7%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Starburst Galaxy	1.7%
Google Cloud Dataflow	3.4%
Other	94.9%

Streaming Analytics

Featured Reviews

Mohammed Aaqibuddin

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.

Read full review

Niketan Kumar

Advisory Solutions Architect at Dell Technologies

Unified data querying has accelerated petabyte-scale analytics and simplified dashboard delivery

Starburst Galaxy offers me several best features, which include very fast querying results, automatic indexing of data for long tables, a cost-based optimizer which reduces the time to query large tables, and an agentic feature that lets me talk to my data.I find myself relying most on querying from different databases as well as automatic indexing in my day-to-day work, as I am a data science architect who needs to get the queries in a very short period of time. Starburst Galaxy serves the best purpose for me because if my SLAs are not met with my customers, they will raise a case, and I have tried many other tools, but Starburst Galaxy fits the best. Starburst Galaxy has positively impacted my organization since we were struggling with Denodo and Dremio, which had their own features but were not helpful in querying large amounts of data, especially semi-structured or unstructured data. Starburst Galaxy addresses this with many YAML files and manifest files for automated maintenance, and it helps reduce the small file problem in different HDFS systems. Additionally, Starburst Galaxy has an MCP server that connects to various agentic pipelines, reducing the time to market for data consumption.

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

"Google Cloud Dataflow has made it very easy for detailed monitoring and logging features for pipeline performance assessment."

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

"Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%."

"I would rate the overall solution a ten out of ten."

"Google's support team is good at resolving issues, especially with large data."

"The support team is good and it's easy to use."

More Google Cloud Dataflow pros

"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 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 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 serves the best purpose for me because if my SLAs are not met with my customers, they will raise a case, and I have tried many other tools, but Starburst Galaxy fits the best."

"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."

"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."

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

More Starburst Galaxy pros

Cons

"The deployment time could also be reduced."

"Google Cloud Dataflow should include a little cost optimization."

"They should do a market survey and then make improvements."

"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 solution's setup process could be more accessible."

"Currently, not all error logs are available to users and this could make debugging failed jobs very difficult."

"There are certain challenges regarding the Google Cloud Composer which can be improved."

"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."

More Google Cloud Dataflow cons

"Starburst Galaxy can be improved by discovering unstructured data and building in streaming ingestion because we are currently using Kafka for that purpose."

"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."

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

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

"I would like Starburst to leverage AI to improve usability. Data lakes are complicated and difficult for users to explore."

"As a hosted option, I wish I had more control over the cluster configuration, specifically regarding some of the more advanced options."

"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."

"I think there are areas of improvement with respect to AI adaptability, and also in general, the amount of connectors working with other tools are areas where it can be expanded."

More Starburst Galaxy cons

Pricing and Cost Advice

"The solution is cost-effective."

"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."

"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."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

"Google Cloud is slightly cheaper than AWS."

"The solution is not very expensive."

"The tool is cheap."

"Google Cloud Dataflow is a cheap solution."

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

913,683 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

19%

Manufacturing Company

12%

Comms Service Provider

Construction Company

Financial Services Firm

25%

Computer Software Company

11%

Manufacturing Company

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	12

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	3

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.

See all answers

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...

See all answers

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...

See all answers

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.

See all answers

What needs improvement with Starburst Galaxy?

Starburst Galaxy can be improved by discovering unstructured data and building in streaming ingestion because we are currently using Kafka for that purpose. We rely on third-party tools for ingesti...

See all answers

What is your primary use case for Starburst Galaxy?

My main use case for Starburst Galaxy is querying petabytes of data across vast data sources, and I use a federated query engine to join data sources from different databases and then join them usi...

See all answers

Comparisons

Databricks vs Google Cloud Dataflow

Compared 13% of the time

Azure Stream Analytics vs Google Cloud Dataflow

Compared 11% of the time

Apache Flink vs Google Cloud Dataflow

Compared 10% of the time

Qlik Talend Cloud vs Google Cloud Dataflow

Compared 10% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 6% of the time

More Google Cloud Dataflow Competitors

Starburst Enterprise vs Starburst Galaxy

Compared 19% of the time

Databricks vs Starburst Galaxy

Compared 9% of the time

Dremio vs Starburst Galaxy

Compared 6% of the time

Qlik Talend Cloud vs Starburst Galaxy

Compared 6% of the time

Altair RapidMiner vs Starburst Galaxy

Compared 5% of the time

More Starburst Galaxy Competitors

Product Reports

Buyer's Guide

Google Cloud Dataflow

September 2026

Download Google Cloud Dataflow product report

Buyer's Guide

Starburst Galaxy

August 2026

Download Starburst Galaxy product report

Also Known As

Google Dataflow

No data available

Overview

Google Cloud Dataflow provides scalable batch and streaming data processing with Apache Beam integration, supporting Python and Java. It's designed for efficient data transformations, analytics, and machine learning, featuring cost-effective serverless operations.

Google Cloud Dataflow is a robust tool for handling large-scale data processing tasks with flexibility in processing batch and streaming workloads. It integrates seamlessly with other Google Cloud services like Pub/Sub for real-time messaging and BigQuery for advanced analytics. The platform supports a wide array of data transformation and preparation needs, making it suitable for complex data workflows and machine learning applications. Despite its advantages, users have noted challenges such as incomplete error logs, longer job startup times, and some limitations in the Python SDK.

What are the key features of Google Cloud Dataflow?

Apache Beam Integration: Allows for advanced data processing capabilities with extensive library support.
Flexible Language Support: Works seamlessly with Python and Java for diverse application requirements.
Scalable Processing: Manages both batch and streaming data efficiently to meet varying data loads.
Cost-Effective Model: Operates on a pay-as-you-go basis, optimizing resource expenditure.
Monitoring Tools: Provides comprehensive assessments to enhance pipeline performance.

What benefits do users experience with Google Cloud Dataflow?

Real-Time Analytics: Facilitates timely data insights essential for fast decision-making.
Integrated Ecosystem: Simplifies orchestration with services like Cloud Composer, enhancing workflow connectivity.
Data Transformation: Enhances machine learning models preparation with robust data cleansing capabilities.

Industries, especially in retail and eCommerce, implement Google Cloud Dataflow for effective batch job execution, data transformation, and event stream processing. It aids in constructing distributed data pipelines for handling extensive analytics tasks, supporting effective large-scale data-driven decisions.

Google

Starburst Galaxy offers rapid query speeds and robust cluster management, enhancing data engineering efficiency while supporting AWS integrations and cross-database functionality. Users benefit from its advanced data integration and federated querying capabilities.

Starburst Galaxy stands out with a compute-focused architecture that excels in facilitating seamless data integration. Technological innovations like autoscaling clusters and automated metadata management optimize operations in multi-tenant environments. With a keen emphasis on compatibility, the platform provides support for AWS Glue and enables federated querying across S3, Snowflake, and Redshift. This adaptability ensures comprehensive ETL processes and enhances analytics through querying SQL Server, Google Sheets, and blob stores. While noted for its robust capabilities, users seek improvements in cluster startup times, Tableau and AI support, and desire infrastructure-as-code enhancements.

What are Starburst Galaxy's key features?

Rapid Query Speeds: Enhances data processing with quick responses.
Cluster Autoscaling: Optimizes resource allocation for varying workloads.
Federated Querying: Cross-database functionality for seamless integration.
Compute-Focused Architecture: Ensures efficiency and scalability.
Automated Metadata Management: Streamlines data handling processes.
AWS Integration: Broad compatibility with AWS services.

What benefits should users evaluate in reviews?

Data Engineering Efficiency: Reduces operating expenses and improves productivity.
Cost Reduction: Highlights lower storage costs with efficient data handling.
Seamless Integration: Enables robust data integration across various platforms.
Cluster Management: Supports diverse workloads with advanced management tools.

In industries focusing on large-scale data efforts, Starburst Galaxy plays an essential role in connecting data sources like Amazon S3 and RDS, streamlining tasks in data engineering and ad-hoc analysis across complex environments. Teams leverage its cross-database querying to boost AWS analytics, with features tailored for sectors needing agile data solutions, from ETL pipelines to secure data federation.

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 2026

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 2026.

DOWNLOAD NOW

913,683 professionals have used our research since 2012.

See our Google Cloud Dataflow vs. Starburst Galaxy report.

See our list of best Streaming Analytics vendors.

We monitor all Streaming Analytics 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.