Google Cloud Dataflow vs Starburst Enterprise comparison

Google and Starburst are both solutions in the Streaming Analytics category. Google is ranked #7 with an average rating of 8.0, while Starburst is ranked #12 with an average rating of 8.5. Google holds a 7.4% mindshare in SA, compared to Starburst’s 3.2% mindshare. Additionally, 92% of Google users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Google Cloud Dataflow

Read 13 Google Cloud Dataflow reviews

3,122 Views
2,513 Comparison Views

92% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

799 Views
685 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Starburst Enterprise

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Executive SummaryUpdated on Dec 17, 2024

Google Cloud Dataflow and Starburst Enterprise are competing in the data processing and analytics space. Starburst Enterprise has an edge due to its comprehensive feature depth and performance in distributed data queries.

Features: Google Cloud Dataflow integrates seamlessly with Google Cloud services like BigQuery. It offers scalability in stream and batch processing and automated resource management. Starburst Enterprise focuses on SQL-based distributed queries, offers broad data source connectivity, and executes federated queries. It is versatile in feature offerings.

Ease of Deployment and Customer Service: Starburst Enterprise offers flexible deployment across on-premises and cloud systems, supported by strong customer service. Google Cloud Dataflow simplifies initial setup due to its cloud-native design but requires complex configurations for advanced scenarios. Starburst provides better customer support and deployment flexibility.

Pricing and ROI: Google Cloud Dataflow is cost-effective for processing within Google's ecosystem, providing high returns when integrated services are used. Starburst Enterprise requires a higher initial investment but yields significant ROI by efficiently handling complex, multi-source data queries. While Google Cloud Dataflow is budget-friendly in specific environments, Starburst shows a strong ROI for high-demand data processing.

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

Buyer's Guide

Google Cloud Dataflow vs. Starburst Enterprise

March 2025

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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

7th

Average Rating

8.0

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Starburst Enterprise

Ranking in Streaming Analytics

12th

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Data Science Platforms (14th)

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Google Cloud Dataflow is 7.4%, up from 7.0% compared to the previous year. The mindshare of Starburst Enterprise is 3.2%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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.

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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.

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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."

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

"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."

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

"Google Cloud Dataflow is useful for streaming and data pipelines."

"The best feature of Google Cloud Dataflow is its practical connectedness."

More Google Cloud Dataflow pros

"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

"The technical support has slight room for improvement."

"The authentication part of the product is an area of concern where improvements are required."

"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."

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

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

"The deployment time could also be reduced."

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

"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."

More Google Cloud Dataflow cons

"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."

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

Pricing and Cost Advice

"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 solution is cost-effective."

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

"Google Cloud Dataflow is a cheap solution."

"The tool is cheap."

"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."

"The solution is not very expensive."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Manufacturing Company

12%

Retailer

11%

Computer Software Company

11%

Financial Services Firm

44%

Computer Software Company

10%

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 Dataflow?

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

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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.

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What needs improvement with Google Cloud Dataflow?

I am not sure, as we built only one job, and it is running on a daily basis. Everything else is managed using BigQuery schedulers and Talend. However, occasionally, dealing with a huge volume of da...

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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...

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Compared 15% of the time

Alteryx vs Starburst Enterprise

Compared 10% of the time

Apache Spark Streaming vs Starburst Enterprise

Compared 4% of the time

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Product Reports

Buyer's Guide

Google Cloud Dataflow

April 2025

Download Google Cloud Dataflow product report

Buyer's Guide

Data Science Platforms

March 2025

Download Starburst Enterprise 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 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

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 Enterprise

March 2025

Free Report: Google Cloud Dataflow vs. Starburst Enterprise

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