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Amazon MSK vs Google Cloud Dataflow comparison

Amazon Web Services (AWS) and Google are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #6 with an average rating of 7.1, while Google is ranked #8 with an average rating of 8.1. Amazon Web Services (AWS) holds a 6.1% mindshare in SA, compared to Google’s 5.5% mindshare. Additionally, 93% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 93% of Google users who would recommend it.
Amazon MSK
Read 14 Amazon MSK reviews
  • 2,786 Views
  • 2,730 Comparison Views
93% willing to recommend
Google Cloud Dataflow
Read 14 Google Cloud Dataflow reviews
  • 2,682 Views
  • 2,226 Comparison Views
93% willing to recommend
 

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Google Cloud Dataflow and Amazon MSK compete in data processing and streaming solutions. Google Cloud Dataflow is noted for high scalability and ease of use, while Amazon MSK is recognized for robust real-time streaming features and reliability.

Features: Google Cloud Dataflow integrates smoothly with Google services, offers excellent scalability, and supports complex data processing. Amazon MSK provides real-time Kafka streaming, integrates easily with AWS services, and ensures strong security.

Room for Improvement: Google Cloud Dataflow could improve its real-time streaming capabilities and offer more granular control settings. Amazon MSK could enhance user-friendliness for non-AWS users and provide more straightforward deployment options. Both could benefit from additional third-party integrations.

Ease of Deployment and Customer Service: Google Cloud Dataflow is known for easy deployment within the Google Cloud ecosystem and responsive customer support. Amazon MSK offers detailed configuration options and a reliable support network, focusing on more controlled setups.

Pricing and ROI: Google Cloud Dataflow's transparent pricing allows flexible cost management, yielding strong ROI. Amazon MSK, while having higher initial costs, offers substantial ROI via efficient real-time data streaming. Dataflow's cost-effectiveness contrasts with MSK's investment in real-time streaming capabilities.

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To learn more, read our detailed Amazon MSK vs. Google Cloud Dataflow Report (Updated: July 2025).
Buyer's Guide
Amazon MSK vs. Google Cloud Dataflow
July 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:
 

ROI

Sentiment score
4.1
Amazon MSK saves time and money, promotes innovation with flexible pricing, yet some users find costs hard to justify.
Sentiment score
5.6
Google Cloud Dataflow was appreciated for cost savings and time efficiency, though some considered its impact not fully assessable yet.
 

Customer Service

Sentiment score
6.2
Amazon MSK support varies; some praise its effectiveness, while others note average service and seek external assistance.
Sentiment score
6.6
Google Cloud Dataflow support varies, with users praising technical resolution but highlighting inconsistent response times and accessibility.
They can manage most of our queries, and for what they cannot manage, they guide us through the process of finding out.
Amazon's support is excellent.
For more quotes and insights, download the Amazon MSK report
The fact that no interaction is needed shows their great support since I don't face issues.
Google's support team is good at resolving issues, especially with large data.
Whenever we have issues, we can consult with Google.
For more quotes and insights, download the Google Cloud Dataflow report
SYED SHAAZ - PeerSpot reviewer
PP
Jana Polianskaja - PeerSpot reviewer
PR
SM
 

Scalability Issues

Sentiment score
6.0
Amazon MSK is viewed as scalable, manageable, appealing to various environments, but may require manual scaling adjustments.
Sentiment score
7.3
Google Cloud Dataflow excels in scalability and efficiency, making it ideal for real-time data processing and dynamic needs.
The functionality for scaling comes out of the box and is very effective.
As a B2B enterprise client, our clientele consists of large ticket clients but low amounts of users.
For more quotes and insights, download the Amazon MSK report
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
For more quotes and insights, download the Google Cloud Dataflow report
PP
SYED SHAAZ - PeerSpot reviewerJana Polianskaja - PeerSpot reviewer
SM
PR
 

Stability Issues

Sentiment score
7.3
Amazon MSK is stable with high ratings; minor issues are unrelated to MSK, maintaining success rates of 91% under load.
Sentiment score
8.3
Google Cloud Dataflow is stable, reliably handles tasks, and benefits from automatic scaling, with minor issues on complex tasks.
It doesn't require any maintenance on my end yet, as I haven't had any issues.
For more quotes and insights, download the Amazon MSK report
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
The job we built has not failed once over six to seven months.
The automatic scaling feature helps maintain stability.
For more quotes and insights, download the Google Cloud Dataflow report
Mauro Velasco Sarmiento - PeerSpot reviewerJana Polianskaja - PeerSpot reviewer
SM
PR
 

Room For Improvement

Amazon MSK struggles with integration, configuration complexities, high costs, and operational challenges compared to services like Kinesis.
Google Cloud Dataflow needs better Kafka integration, improved error logs, reduced startup time, and enhanced Python SDK features.
The increase in cloud costs by 50% to 60% does not justify the savings.
The only issue with Amazon MSK that we are facing is the configurations.
I had to remove and drop all the clusters and recreate them again, which is complicated in a production environment.
For more quotes and insights, download the Amazon MSK report
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
Dealing with a huge volume of data causes failure due to array size.
I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.
For more quotes and insights, download the Google Cloud Dataflow report
PP
SYED SHAAZ - PeerSpot reviewerMauro Velasco Sarmiento - PeerSpot reviewerJana Polianskaja - PeerSpot reviewer
SM
PR
 

Setup Cost

Amazon MSK is competitively priced with variable costs, appealing to enterprise users but potentially expensive for some.
Google Cloud Dataflow is praised for cost-effectiveness and scalability, offering competitive pricing influenced by pipeline complexity and company size.
Once we started using Kafka, our cloud costs rose by 50% to 60%.
We use Kafka M5 Large instance, and depending on the instances, that is the cost we have, along with storage cost and data transfer costs.
For more quotes and insights, download the Amazon MSK report
It is part of a package received from Google, and they are not charging us too high.
For more quotes and insights, download the Google Cloud Dataflow report
PP
SYED SHAAZ - PeerSpot reviewer
SM
 

Valuable Features

Amazon MSK integrates AWS services, offering cost-effective, automated scaling, ease of use, real-time analytics, encryption, and event sourcing.
Google Cloud Dataflow offers seamless integration, multi-language support, scalability, and serverless data handling for efficient batch and streaming processes.
The scalability and usability are quite remarkable.
The best features of Amazon MSK are the real-time analytics that are excellent.
Amazon MSK is basically Kafka in the cloud, and when you need to create a cluster of Kafka brokers, Amazon MSK helps with that by automatically creating all the brokers according to the configuration you provide.
For more quotes and insights, download the Amazon MSK report
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts.
For more quotes and insights, download the Google Cloud Dataflow report
PP
SYED SHAAZ - PeerSpot reviewerMauro Velasco Sarmiento - PeerSpot reviewerJana Polianskaja - PeerSpot reviewer
SM
PR
 

Categories and Ranking

Amazon MSK
Ranking in Streaming Analytics
6th
Average Rating
7.2
Reviews Sentiment
6.5
Number of Reviews
14
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
8th
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of September 2025, in the Streaming Analytics category, the mindshare of Amazon MSK is 6.1%, down from 9.3% compared to the previous year. The mindshare of Google Cloud Dataflow is 5.5%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Market Share Distribution
ProductMarket Share (%)
Amazon MSK6.1%
Google Cloud Dataflow5.5%
Other88.4%
Streaming Analytics
 

Featured Reviews

SYED SHAAZ - PeerSpot reviewer
Improved data streaming and integration challenges prompt search for alternatives
The integration capabilities of Amazon MSK are not very flexible. If you have your own self-managed Kafka, that helps significantly because you can set up configurations. We are considering self-managed Kafka since our product is only one year old. The Kafka integrations are fine, but the configurations are an issue. The only issue with Amazon MSK that we are facing is the configurations. There are preset configurations and limited configurations that we can set for our unique use case. The product could improve by allowing us to set different configurations. I would also like to see Amazon MSK improve in the area of connectors. We are considering Confluent Cloud because they have many more connectors. They have KSQL DB and governance features. It is slightly costlier, but Confluent offers more flexibility with their connectors.
Read full review
Jana Polianskaja - PeerSpot reviewer
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
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Computer Software Company
15%
Manufacturing Company
6%
Comms Service Provider
5%
Financial Services Firm
17%
Manufacturing Company
12%
Retailer
11%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise7
Large Enterprise4
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise10
 

Questions from the Community

What do you like most about Amazon MSK?
Amazon MSK has significantly improved our organization by building seamless integration between systems.
See all answers
What needs improvement with Amazon MSK?
The integration capabilities of Amazon MSK are not very flexible. If you have your own self-managed Kafka, that helps significantly because you can set up configurations. We are considering self-ma...
See all answers
What is your primary use case for Amazon MSK?
We are recently working with Amazon MSK at Fortis, where we have multiple dashboards in our revenue intelligence platform. We are streaming data from different apps into those dashboards. The data ...
See all answers
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
 

Comparisons

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Confluent vs Amazon MSK Logo
Confluent vs Amazon MSK
Compared 18% of the time
Apache Flink vs Amazon MSK Logo
Apache Flink vs Amazon MSK
Compared 16% of the time
Amazon Kinesis vs Amazon MSK Logo
Amazon Kinesis vs Amazon MSK
Compared 7% of the time
Apache Pulsar vs Amazon MSK Logo
Apache Pulsar vs Amazon MSK
Compared 6% of the time
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Databricks vs Google Cloud Dataflow Logo
Databricks vs Google Cloud Dataflow
Compared 35% of the time
Apache Flink vs Google Cloud Dataflow Logo
Apache Flink vs Google Cloud Dataflow
Compared 21% of the time
Apache NiFi vs Google Cloud Dataflow Logo
Apache NiFi vs Google Cloud Dataflow
Compared 11% of the time
Spring Cloud Data Flow vs Google Cloud Dataflow Logo
Spring Cloud Data Flow vs Google Cloud Dataflow
Compared 7% of the time
AWS Lambda vs Google Cloud Dataflow Logo
AWS Lambda vs Google Cloud Dataflow
Compared 4% of the time
More Google Cloud Dataflow Competitors
 

Product Reports

Buyer's Guide
Amazon MSK
September 2025
Amazon MSK reportDownload Amazon MSK product report
Buyer's Guide
Google Cloud Dataflow
September 2025
Google Cloud Dataflow reportDownload Google Cloud Dataflow product report
 

Also Known As

Amazon Managed Streaming for Apache Kafka
Google Dataflow
 

Overview

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.

Amazon Web Services (AWS)
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
 

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Buyer's Guide
Amazon MSK vs. Google Cloud Dataflow
July 2025
Free Report: Amazon MSK vs. Google Cloud Dataflow
Find out what your peers are saying about Amazon MSK vs. Google Cloud Dataflow and other solutions. Updated: July 2025.
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