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Aiven Platform vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive Summary

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

Aiven Platform
Ranking in Streaming Analytics
13th
Average Rating
8.0
Reviews Sentiment
6.5
Number of Reviews
1
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
8th
Average Rating
8.0
Reviews Sentiment
7.3
Number of Reviews
11
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2025, in the Streaming Analytics category, the mindshare of Aiven Platform is 1.3%, up from 1.2% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.7%, up from 6.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

reviewer2539719 - PeerSpot reviewer
Faster and easier to use with less maintenance
We haven't had any significant issues so far. Our manager can escalate issues to the solution's engineers even when we have issues. They provide a fast response to our incidents and solve our issues. After solving the problem, the engineers also look into the issue to find the root cause. We've contacted their support before. We submit support tickets and follow up with our account manager. The account manager can then follow up with the engineers, which helps the ticket get processed faster. Their support team has several experts, especially in Apache Kafka. We can always ask them about specific issues. For example, our principal engineer once asked about Kafka offset, and the account manager was able to handle it and provide best practices.
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"What I like best about the tool is that the process for the services is faster compared to other methods. It's easier to use because Aiven for Apache Kafka handles the maintenance, so we have less to manage. We only use Kafka to manage its connectivity."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"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 solution allows us to program in any language we desire."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"I would rate the overall solution a ten out of ten."
"The service is relatively cheap compared to other batch-processing engines."
"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."
 

Cons

"One challenge we face is when we want to update the version, for example, from 3.6 to 3.7. It will spawn new nodes, and then there's rebalancing and syncing from other brokers. There's high CPU usage during this process, so the solution can't be used for a while, causing some downtime in our services. To tackle this challenge, we schedule maintenance updates during low-traffic periods when there's less risk and fewer users use the services."
"The deployment time could also be reduced."
"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."
"They should do a market survey and then make improvements."
"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."
"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."
"The solution's setup process could be more accessible."
 

Pricing and Cost Advice

Information not available
"Google Cloud Dataflow is a cheap solution."
"The tool is cheap."
"The solution is not very expensive."
"The solution is cost-effective."
"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 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."
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Top Industries

By visitors reading reviews
Computer Software Company
24%
Financial Services Firm
15%
Real Estate/Law Firm
7%
Retailer
6%
Financial Services Firm
17%
Retailer
12%
Manufacturing Company
12%
Computer Software Company
12%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What needs improvement with Aiven for Apache Kafka?
One challenge we face is when we want to update the version, for example, from 3.6 to 3.7. It will spawn new nodes, and then there's rebalancing and syncing from other brokers. There's high CPU usa...
What is your primary use case for Aiven for Apache Kafka?
I use Aiven for Apache Kafka to handle services dependent on each other. Instead of API calls, we use it for asynchronous processing of all our services. That's the general way we use it.
What advice do you have for others considering Aiven for Apache Kafka?
Apache Kafka is really good for streaming data or messaging services. It's comparable to other messaging services. Overall, I'd rate Aiven for Apache Kafka an eight out of ten.
What do you like most about Google Cloud Dataflow?
The product's installation process is easy...The tool's maintenance part is somewhat easy.
What needs improvement with Google Cloud Dataflow?
The authentication part of the product is an area of concern where improvements are required. For some common users, the solution's authentication part is difficult to use. The scalability of the p...
 

Also Known As

No data available
Google Dataflow
 

Overview

 

Sample Customers

Information Not Available
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Microsoft and others in Streaming Analytics. Updated: January 2025.
837,501 professionals have used our research since 2012.