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Aiven Platform vs Spring Cloud Data Flow comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Aiven Platform
Ranking in Streaming Analytics
13th
Average Rating
8.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
Spring Cloud Data Flow
Ranking in Streaming Analytics
10th
Average Rating
7.8
Number of Reviews
8
Ranking in other categories
Data Integration (22nd)
 

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Aiven Platform is 1.4%, down from 1.4% compared to the previous year. The mindshare of Spring Cloud Data Flow is 5.0%, up from 4.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

MR
Aug 30, 2024
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.
NitinGoyal - PeerSpot reviewer
Aug 15, 2024
Has a plug-and-play model and provides good robustness and scalability
The solution's community support could be improved. I don't know why the Spring Cloud Data Flow community is not very strong. Community support is very limited whenever you face any problem or are stuck somewhere. I'm not sure whether it has improved in the last six months because this pipeline was set up almost two years ago. I struggled with that a lot. For example, there was limited support whenever I got an exception and sought help from Stack Overflow or different forums. Interacting with Kubernetes needs a few certificates. You need to define all the certificates within your application. With the help of those certificates, your Java application or Spring Cloud Data Flow can interact with Kubernetes. I faced a lot of hurdles while placing those certificates. Despite following the official documentation to define all the replicas, readiness, and liveliness probes within the Spring Cloud Data Flow application, it was not working. So, I had to troubleshoot while digging in and debugging the internals of Spring Cloud Data Flow at that time. It was just a configuration mismatch, and I was doing nothing weird. There was a small spelling difference between how Spring Cloud Data Flow was expecting it and how I passed it. I was just following the official documentation.

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 dashboards in Spring Cloud Dataflow are quite valuable."
"The most valuable feature is real-time streaming."
"There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure."
"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."
"The solution's most valuable feature is that it allows us to use different batch data sources, retrieve the data, and then do the data processing, after which we can convert and store it in the target."
"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like."
"The product is very user-friendly."
 

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."
"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"The solution's community support could be improved."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications."
"I would improve the dashboard features as they are not very user-friendly."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
 

Pricing and Cost Advice

Information not available
"The solution provides value for money, and we are currently using its community edition."
"This is an open-source product that can be used free of charge."
"If you want support from Spring Cloud Data Flow there is a fee. The Spring Framework is open-source and this is a free solution."
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Top Industries

By visitors reading reviews
Computer Software Company
23%
Financial Services Firm
13%
Manufacturing Company
7%
Educational Organization
7%
Financial Services Firm
29%
Computer Software Company
16%
Manufacturing Company
7%
Retailer
7%
 

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 needs improvement with Spring Cloud Data Flow?
I would improve the dashboard features as they are not very user-friendly. Another area for improvement is the documentation, as it is not very precise. There are limited resources available on Spr...
What is your primary use case for Spring Cloud Data Flow?
I am a developer using Spring Cloud Dataflow. We primarily use it to convert our applications from monolithic to microservices. The solution is used for scheduling tasks in a specific order and ens...
What advice do you have for others considering Spring Cloud Data Flow?
My advice would be to thoroughly review the documentation and understand if Spring Cloud Dataflow is the right solution for your application. For applications with only one or two microservices, it...
 

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