Google Cloud Dataflow vs Striim comparison

Google and Striim are both solutions in the Streaming Analytics category. Google is ranked #13 with an average rating of 8.4, while Striim is ranked #23 with an average rating of 8.0. Google holds a 3.4% 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

2,989 Views
2,571 Comparison Views

93% willing to recommend

Striim

Read 2 Striim reviews

1,932 Views
638 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Striim

Comparison Buyer's Guide

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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: July 2026).

Buyer's Guide

Streaming Analytics

July 2026

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Helped 909,647 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

Striim

Ranking in Streaming Analytics

23rd

Average Rating

8.0

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

Data Integration (47th), Cloud Data Integration (27th)

Mindshare comparison

As of August 2026, in the Streaming Analytics category, the mindshare of Google Cloud Dataflow is 3.4%, down from 5.9% compared to the previous year. The mindshare of Striim is 1.7%, up from 0.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Google Cloud Dataflow	3.4%
Striim	1.7%
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.

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Ramakanth Vanga

Data Engineer

Real-time data capture has accelerated releases and now improves trust in our data warehouse

The checkpoints would help me to figure out where the problem was if there's any lag, but I had to do a lot of manual work to figure out where the lag is. Striim would not intuitively tell me the culprit table or database behind the lag. I believe that is an improvement Striim could definitely do. Passwords were an issue. Property variables were not supported for passwords, meaning I had to make sure the password is manually populated. I believe if Striim could read from AWS secrets or its own secret mechanism to store the password, that would really save a lot of time so that I don't have to keep updating the password whenever there is any change. The user experience of triggering alerts if there's any lag which Striim identified, which is outside normal processing time, could intrinsically be done by Striim. I believe that was lacking. I would wait for Striim to tell me, instead of me going and validating whether Striim is lagging behind. If Striim could itself tell me that it's seeing a lot more volume than expected, that would really make me give it a higher number.

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

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

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

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

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

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

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

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."

"We were confidently in a situation to call Snowflake as a single source of truth, and I believe with Striim, we were able to do that because without Striim, the SLA would be much higher, and there would not have been much confidence in Snowflake."

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."

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

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

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

"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."

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

"Occasionally, dealing with a huge volume of data causes failure due to array size."

More Google Cloud Dataflow cons

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

"The user experience of triggering alerts if there's any lag which Striim identified, which is not normal, could intrinsically be done by Striim."

Pricing and Cost Advice

"Google Cloud Dataflow is a cheap solution."

"Google Cloud is slightly cheaper than AWS."

"The solution is cost-effective."

"The solution is not very expensive."

"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."

"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 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."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

12%

Comms Service Provider

Retailer

Construction Company

16%

Healthcare Company

15%

Retailer

12%

Financial Services Firm

10%

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...

See all answers

What is your experience regarding pricing and costs for Striim?

My experience with the pricing, implementation cost, and licensing of Striim is that it is somewhat expensive.

See all answers

What needs improvement with Striim?

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

See all answers

What is your primary use case for Striim?

I use Striim to perform change data capture from relational databases to non-relational databases in my organization. I implement CDC with Striim by transferring data from Oracle Database to MongoD...

See all answers

Comparisons

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Compared 14% of the time

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Compared 12% of the time

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Compared 11% of the time

Qlik Talend Cloud vs Google Cloud Dataflow

Compared 10% of the time

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Compared 14% of the time

Upsolver vs Striim

Compared 10% of the time

Oracle GoldenGate vs Striim

Compared 10% of the time

AWS Database Migration Service vs Striim

Compared 10% of the time

Databricks vs Striim

Compared 3% of the time

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Product Reports

Buyer's Guide

Google Cloud Dataflow

August 2026

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Buyer's Guide

Streaming Analytics

July 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: July 2026.

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