Google Cloud Dataflow vs Upsolver comparison

Why PeerSpot?

Google and Upsolver are both solutions in the Streaming Analytics category. Google is ranked #12 with an average rating of 8.4, while Upsolver is ranked #22 with an average rating of 8.7. Google holds a 3.5% mindshare in SA, compared to Upsolver’s 1.3% 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 15 Google Cloud Dataflow reviews

3,224 Views
2,773 Comparison Views

93% willing to recommend

Upsolver

Read 4 Upsolver reviews

2,415 Views
628 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 2026).

Buyer's Guide

Google Cloud Dataflow vs. Upsolver

September 2026

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Helped 915,341 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

12th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Upsolver

Ranking in Streaming Analytics

22nd

Average Rating

8.6

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

Data Integration (39th)

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 Upsolver is 1.3%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Google Cloud Dataflow	3.5%
Upsolver	1.3%
Other	95.2%

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.

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reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

Streaming pipelines have become simpler and onboarding new data sources is now much faster

One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

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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

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

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

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

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

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

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

"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."

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

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."

"I have saved 50 to 60% on maintaining pipelines since using Upsolver."

"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."

Cons

"The technical support has slight room for improvement."

"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."

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

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

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

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

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

More Google Cloud Dataflow cons

"There is room for improvement in query tuning."

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

"I would say Upsolver's scalability is eight out of 10 because of pricing."

"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

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

"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 tool is cheap."

"The solution is not very expensive."

"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 Google Cloud Dataflow's pricing a four out of ten."

"The solution is cost-effective."

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

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915,341 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

19%

Manufacturing Company

13%

Comms Service Provider

Construction Company

Real Estate/Law Firm

14%

Manufacturing Company

14%

Retailer

13%

Construction Company

11%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

No data available

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.

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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...

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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...

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What is your experience regarding pricing and costs for Upsolver?

My experience with pricing, setup cost, and licensing is that the pricing is nine out of 10.

See all answers

What needs improvement with Upsolver?

I think Upsolver can be improved with deeper integration with external orchestration out of the box. I would appreciate more clear dashboards with billing in real time as a needed improvement.

See all answers

What is your primary use case for Upsolver?

My main use case for Upsolver is to operate with changes in the structure of new data without a pipeline disrupting. I write SQL queries in Upsolver, and the platform takes care of the data itself,...

See all answers

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Compared 9% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 6% of the time

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Compared 10% of the time

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Compared 10% of the time

Rivery vs Upsolver

Compared 7% of the time

SAS Access vs Upsolver

Compared 6% of the time

Alteryx Designer vs Upsolver

Compared 5% of the time

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Product Reports

Buyer's Guide

Google Cloud Dataflow

September 2026

Download Google Cloud Dataflow product report

Buyer's Guide

Data Integration

September 2026

Download Upsolver 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

Upsolver offers a data lakehouse platform that simplifies big data processing and analytics, enabling data teams to efficiently handle large-scale datasets.

Upsolver's platform focuses on simplifying the complexities of data engineering by transforming raw data into queryable formats quickly. It allows seamless integration with cloud storage and various database systems, providing flexibility for data-driven businesses. The platform automates data preparation tasks, reducing manual coding and allowing teams to focus on extracting insights. Its scalable architecture supports real-time analytics and batch processing.

What are the key features of Upsolver?

Data Ingestion: Easily ingest data from multiple sources with minimal configuration.
Schema On-Read: Automatically interpret data schemas, simplifying data management.
Streaming SQL: Transform and analyze streaming data using a familiar SQL syntax.
Data Lake Integration: Seamlessly connect with data lakes for streamlined workflows.

What benefits and ROI should users consider?

Time Savings: Faster data processing reduces time to insights.
Cost Efficiency: Scalable architecture optimizes infrastructure costs.
Scalability: Handle growing data volumes effortlessly.
Flexibility: Supports wide data storage and processing strategies.

In the e-commerce industry, Upsolver helps businesses optimize their data pipelines for better customer segmentation and personalization, while in finance, it enhances fraud detection capabilities through real-time data analytics.

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 2026

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 2026.

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