No more typing reviews! Try our Samantha, our new voice AI agent.

Apache Kafka vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

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
6.3
Apache Kafka offers ROI through scalability, cost reduction, time savings, customization, and valuable insights, despite some challenges.
Sentiment score
4.7
Google Cloud Dataflow offers significant cost and time savings, proving to be an efficient investment for data architecture.
 

Customer Service

Sentiment score
6.0
Apache Kafka primarily depends on an active open-source community for support, complemented by in-house expertise and optional paid services.
Sentiment score
6.1
Google Cloud Dataflow's support is effective for large issues but experiences mixed feedback on response times and service consistency.
The Apache community provides support for the open-source version.
Technology Leader at eTCaaS
There is plenty of community support available online.
With Microsoft, expectations are higher because we pay for a license and have a contract.
Senior Manager at Timestamp, SA
The fact that no interaction is needed shows their great support since I don't face issues.
Data Engineer at Accenture
Google's support team is good at resolving issues, especially with large data.
Senior Data Engineer at Accruent
Whenever we have issues, we can consult with Google.
Senior Software Engineer at Dun & Bradstreet
 

Scalability Issues

Sentiment score
7.7
Apache Kafka offers scalable solutions with Kubernetes, efficiently handling large data and users across industries, especially finance.
Sentiment score
6.9
Google Cloud Dataflow excels in scalability, resource optimization, and autoscaling, effectively supporting varying data volumes across departments.
Customers have not faced issues with user growth or data streaming needs.
Technology Leader at eTCaaS
Apache Kafka is highly scalable and supports horizontal scaling by allowing you to add more brokers to the cluster and increase the number of partitions for a topic.
Senior Engineer at Airy Software Technologies Private Limited
I need to enable my solution with high availability and scalability.
Data Architect at Ascendion
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
Data Engineer at Accenture
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
Senior Software Engineer at Dun & Bradstreet
Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
Senior Data Engineer at Accruent
 

Stability Issues

Sentiment score
7.6
Apache Kafka is stable and reliable, efficiently handling high data volumes with minimal issues and high user satisfaction.
Sentiment score
8.3
Google Cloud Dataflow is stable and reliable, praised for automatic scaling, despite occasional errors with complex tasks.
Apache Kafka is stable.
Technology Leader at eTCaaS
This feature of Apache Kafka has helped enhance our system stability when handling high volume data.
DevOps Engineer
Apache Kafka is more stable and is very good technology for asynchronous programming.
Senior Engineer at Airy Software Technologies Private Limited
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
Data Engineer at Accenture
The job we built has not failed once over six to seven months.
Senior Software Engineer at Dun & Bradstreet
The automatic scaling feature helps maintain stability.
Senior Data Engineer at Accruent
 

Room For Improvement

Kafka needs improvements in duplicate management, UI, troubleshooting, cloud integration, messaging control, ZooKeeper dependency, and management tools.
Improvements in error logging, support, cost, integration, scalability, and automation are needed for Google Cloud Dataflow's efficiency.
Operating an Apache Kafka cluster requires expertise in partitioning, application monitoring, and capacity planning.
Senior Engineer at Airy Software Technologies Private Limited
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
Technology Leader at eTCaaS
Apache Kafka groups could introduce themes or profiles of configuration to help manage this complexity without needing expertise.
Senior Principal Architect at a computer software company with 501-1,000 employees
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
Data Engineer at Accenture
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 manually.
Senior Customer Data Platform Specialist at a marketing services firm with 1,001-5,000 employees
Dealing with a huge volume of data causes failure due to array size.
Senior Software Engineer at Dun & Bradstreet
 

Setup Cost

Apache Kafka is open-source and affordable, but managed services and support can incur additional costs.
Google Cloud Dataflow is seen as a cost-effective streaming solution, with affordability ratings varying widely among users.
From a price perspective, if you are asking about Apache Kafka, I would rate it a nine.
Senior Principal Architect at a computer software company with 501-1,000 employees
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Technology Leader at eTCaaS
Apache Kafka itself is open source and free to use.
Senior Engineer at Airy Software Technologies Private Limited
It is part of a package received from Google, and they are not charging us too high.
Senior Software Engineer at Dun & Bradstreet
 

Valuable Features

Apache Kafka provides scalable, fault-tolerant, real-time data streaming for reliable message processing and integration across platforms with open-source flexibility.
Google Cloud Dataflow offers scalable, cost-effective data processing, integrating seamlessly with Google Cloud, using Apache Beam and various tools.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
Apache Kafka is particularly valuable for managing high levels of transactions.
Senior Manager at Timestamp, SA
The best features include high throughput with low latency and support for horizontal scaling.
Senior Engineer at Airy Software Technologies Private Limited
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
Data Engineer at Accenture
The integration within Google Cloud Platform is very good.
Senior Software Engineer at Dun & Bradstreet
Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts.
Senior Data Engineer at Accruent
 

Categories and Ranking

Apache Kafka
Ranking in Streaming Analytics
3rd
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
92
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
13th
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
15
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of August 2026, in the Streaming Analytics category, the mindshare of Apache Kafka is 3.8%, up from 3.5% compared to the previous year. The mindshare of Google Cloud Dataflow is 3.4%, down from 5.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Apache Kafka3.8%
Google Cloud Dataflow3.4%
Other92.8%
Streaming Analytics
 

Featured Reviews

Amandeep Pawar - PeerSpot reviewer
Senior Engineer at Airy Software Technologies Private Limited
Event-driven architecture has improved asynchronous communication and supports high throughput
We can improve the high throughput because there are some limitations. We could add something to improve Apache Kafka. Based on my daily usage and analysis, there is a complex setup and management, which is one area requiring improvement. Apache Kafka does not have built-in message delay or scheduling capabilities. Apache Kafka cannot natively schedule messages. Limited message prioritization is also an area requiring improvement. Ordering is limited to a single partition. Large messages affect performance. Apache Kafka is optimized for many small to medium-sized messages, but large payloads increase network usage, increase disk usage, and slow down producers and consumers. I rate Apache Kafka eight out of ten instead of ten out of ten because operating an Apache Kafka cluster requires expertise in partitioning, application monitoring, and capacity planning. Self-managed deployment can become complex as the cluster grows. Pro-managed Apache Kafka services significantly reduce the operational overhead.
reviewer2812851 - PeerSpot reviewer
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.
report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
908,800 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
17%
Outsourcing Company
12%
Manufacturing Company
10%
Construction Company
9%
Financial Services Firm
18%
Manufacturing Company
13%
Retailer
8%
Comms Service Provider
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business33
Midsize Enterprise20
Large Enterprise51
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise12
 

Questions from the Community

What are the differences between Apache Kafka and IBM MQ?
Apache Kafka is open source and can be used for free. It has very good log management and has a way to store the data used for analytics. Apache Kafka is very good if you have a high number of user...
What is your experience regarding pricing and costs for Apache Kafka?
From the AWS perspective, the price is on the higher side. However, if you go for Apache Kafka, it is low. From a price perspective, if you are asking about Apache Kafka, I would rate it a nine.
What needs improvement with Apache Kafka?
Apache Kafka is abundant with features which only an expert-level person will be able to manage due to the high volume and high concurrent expectations. Apache Kafka groups could introduce themes o...
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.
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...
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...
 

Also Known As

No data available
Google Dataflow
 

Overview

 

Sample Customers

Uber, Netflix, Activision, Spotify, Slack, Pinterest
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Apache Kafka vs. Google Cloud Dataflow and other solutions. Updated: June 2026.
908,800 professionals have used our research since 2012.