Google Cloud Dataflow vs Striim comparison

Google and Striim are both solutions in the Streaming Analytics category. Google is ranked #12 with an average rating of 8.4, while Striim is ranked #26. Google holds a 3.5% mindshare in SA, compared to Striim’s 1.7% mindshare. Additionally, 93% of Google users are willing to recommend the solution, compared to 100% of Striim users who would recommend it.

Google Cloud Dataflow

Read 15 Google Cloud Dataflow reviews

2,861 Views
2,460 Comparison Views

93% willing to recommend

Striim

Read 1 Striim review

1,618 Views
518 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Striim

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Executive Summary

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

Find out what your peers are saying about Databricks, Microsoft, Apache and others in Streaming Analytics.

To learn more, read our detailed Streaming Analytics Report (Updated: June 2026).

Buyer's Guide

Streaming Analytics

June 2026

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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

12th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Striim

Ranking in Streaming Analytics

26th

Average Rating

8.0

Reviews Sentiment

5.2

Number of Reviews

Ranking in other categories

Data Integration (65th), Cloud Data Integration (30th)

Mindshare comparison

As of June 2026, in the Streaming Analytics category, the mindshare of Google Cloud Dataflow is 3.5%, down from 6.8% compared to the previous year. The mindshare of Striim is 1.7%, up from 0.5% 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%
Striim	1.7%
Other	94.8%

Streaming Analytics

Featured Reviews

reviewer2812851

Senior Customer Data Platform Specialist at a marketing services firm with 1,001-5,000 employees

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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Mario Rodríguez Hernández

Arquitecto De Soluciones at a tech vendor with 10,001+ employees

Reliable change data capture has kept critical databases synchronized in real time

I consider the best features that Striim offers to be great performance and a large number of sources and targets that can be connected. Striim is capable of absorbing a large number of transactions, and the difference between the two databases is always less than a second, which demonstrates efficiency and highlights the variety of sources and targets I have used. Striim has had a positive impact on my organization as it has solved stability issues that I had with other tools.

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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

"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."

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

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

"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."

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

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

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

More Google Cloud Dataflow pros

"Striim is capable of absorbing a large number of transactions, and the difference between the two databases is always less than a second, which demonstrates efficiency and highlights the variety of sources and targets I have used."

Cons

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

"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."

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

"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."

"The technical support has slight room for improvement."

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

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

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

More Google Cloud Dataflow cons

"I think Striim could be improved with better pricing and enhanced documentation."

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 tool is cheap."

"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."

"The solution is not very expensive."

"Google Cloud Dataflow is a cheap solution."

"The solution is cost-effective."

"Google Cloud is slightly cheaper than AWS."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

20%

Manufacturing Company

12%

Retailer

Computer Software Company

Healthcare Company

15%

Construction Company

15%

Financial Services Firm

13%

Retailer

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.

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...

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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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Compared 9% of the time

Apache Spark vs Google Cloud Dataflow

Compared 3% of the time

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Compared 14% of the time

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Compared 9% of the time

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Compared 9% of the time

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Compared 9% of the time

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Compared 3% of the time

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Product Reports

Buyer's Guide

Google Cloud Dataflow

June 2026

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Buyer's Guide

Streaming Analytics

June 2026

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Also Known As

Google Dataflow

Striim Platform

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

Striim offers a comprehensive platform for real-time data integration and streaming analytics, designed to streamline data processes for enterprise-level solutions.

Striim enables seamless migration and integration of data across cloud and on-premises environments, making it ideal for businesses looking to leverage real-time analytics. Its capabilities support continuous data flow, reducing latency and enhancing decision-making. Designed for scalable and secure data management, Striim facilitates effective data-driven strategies.

What are some key features of Striim?

Continuous Data Ingestion: Collects and processes real-time data streams promptly.
Data Integration: Combines data from different sources for unified insights.
Real-Time Analytics: Supports instant analytics, allowing fast response to changes.
Scalable Architecture: Adapts to large-scale data environments efficiently.

What benefits should users look for in Striim reviews?

Improved Data Accuracy: Ensures data integrity in analytics.
Enhanced Operational Efficiency: Streamlined processes for faster data handling.
Cost-Effective Integration: Reduces expenses associated with traditional data handling.
Flexible Deployment Options: Allows for tailored solutions fitting specific needs.

In industries like finance, Striim supports real-time fraud detection by providing uninterrupted data streaming between transaction systems. In healthcare, it enables rapid data processing for patient monitoring, improving service delivery. Manufacturing uses Striim to enhance supply chain visibility through real-time data analytics.

Striim

Sample Customers

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

Sky, UPS, MACY'S, EMAAR, HSBC

Find out what your peers are saying about Databricks, Microsoft, Apache and others in Streaming Analytics. Updated: June 2026.

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