Google Cloud Dataflow vs Upsolver comparison

Google and Upsolver are both solutions in the Streaming Analytics category. Google is ranked #9 with an average rating of 8.1, while Upsolver is ranked #20 with an average rating of 8.5. Google holds a 5.1% mindshare in SA, compared to Upsolver’s 0.4% mindshare. Additionally, 93% of Google users are willing to recommend the solution, compared to 100% of Upsolver 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

Upsolver

Read 2 Upsolver reviews

369 Views
100 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Upsolver

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Google Cloud Dataflow and Upsolver 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. Upsolver Report (Updated: September 2025).

Buyer's Guide

Google Cloud Dataflow vs. Upsolver

September 2025

Download the complete report

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

Upsolver

Ranking in Streaming Analytics

20th

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Data Integration (40th)

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 Upsolver is 0.4%, up from 0.2% 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%
Upsolver	0.4%
Other	94.5%

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

Snehasish Das

Technology Leader at eTCaaS

Allows for data to be moved across platforms and different data technologies

The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies. Upsolver does this in a quick time, unlike traditional processes which are time-consuming. Additionally, it offers scalability for large volumes of data, with performance and ease of cloud-native integration.

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

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

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

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

"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."

"It is a scalable solution."

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

More Google Cloud Dataflow pros

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."

Cons

"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 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."

"The technical support has slight room for improvement."

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

"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."

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

"The deployment time could also be reduced."

More Google Cloud Dataflow cons

"There is room for improvement in query tuning."

"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."

"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."

Pricing and Cost Advice

"Google Cloud is slightly cheaper than AWS."

"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."

"Google Cloud Dataflow is a cheap solution."

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

"The tool is cheap."

"The solution is not very expensive."

"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."

"Upsolver is affordable at approximately $225 per terabyte per year."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Manufacturing Company

12%

Retailer

10%

Computer Software Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

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.

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 Upsolver?

Upsolver is affordable at approximately $225 per terabyte per year. Compared to what I know from others, it's cheaper than many other products.

See all answers

What needs improvement with Upsolver?

There is room for improvement in query tuning. Upsolver could do a more in-depth analysis in employing machine power, such as CPU and memory, to enhance query performance. Furthermore, allocating C...

See all answers

What is your primary use case for Upsolver?

I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs....

See all answers

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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

Databricks vs Upsolver

Compared 59% of the time

Rivery vs Upsolver

Compared 41% of the time

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Product Reports

Buyer's Guide

Google Cloud Dataflow

September 2025

Download Google Cloud Dataflow product report

Buyer's Guide

Data Integration

September 2025

Download Upsolver 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

Upsolver's mission is to turn engineering-intensive data lakes into easy-to-use repositories that serve 50X more data professionals compared to the status-quo today. Upsolver cuts 95% from the tedious process of preparing event data for analytics and machine learning, using a visual SQL-based interface, a cloud-native data platform and deep technology for stream-processing, indexing and eventually-consistent file systems.

Upsolver

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. Upsolver

September 2025

Free Report: Google Cloud Dataflow vs. Upsolver

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

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