Try our new research platform with insights from 80,000+ expert users

Amazon Kinesis vs Spring Cloud Data Flow comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.0
Number of Reviews
27
Ranking in other categories
No ranking in other categories
Spring Cloud Data Flow
Ranking in Streaming Analytics
10th
Average Rating
7.8
Reviews Sentiment
6.7
Number of Reviews
9
Ranking in other categories
Data Integration (22nd)
 

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 10.6%, down from 15.4% 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

Rajni Kumar Jha - PeerSpot reviewer
Used for media streaming and live-streaming data
It is not compulsory to use Amazon Kinesis. If you don't want to use the data streaming, you can use just the Kinesis data firehose. Using the Kinesis data firehose is compulsory because we can't store all chats and recordings in Amazon S3 without it. When a call comes in the Amazon Kinesis instance, it will go to Data Streams if we use it. Otherwise, it will go to the Kinesis data firehose, where we need to define the S3 bucket path, and it will go to Amazon S3. So, without the Kinesis data firehose, we can't store all the chats and recordings in Amazon S3. Using Amazon Kinesis totally depends upon the user's requirements. If you want to use live streaming for the data lake or data analyst team, you need to use Amazon Kinesis. If you don't want to use it, you can directly use the Kinesis data firehose, which will be stored in Amazon S3. Overall, I rate the solution an eight out of ten.
NitinGoyal - PeerSpot reviewer
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

"From my experience, one of the most valuable features is the ability to track silent events on endpoints. Previously, these events might have gone unnoticed, but now we can access them within the product range. For example, if a customer reports that their calls are not reaching the portal files, we can use this feature to troubleshoot and optimize the system."
"The Kinesis VideoStream and DataStream are the most important features."
"The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"The scalability is pretty good."
"The product's initial setup phase is not difficult because we are using the tool on the cloud."
"Amazon Kinesis has improved our ROI."
"Everything is hosted and simple."
"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 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 feature is real-time streaming."
"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."
"The ease of deployment on Kubernetes, the seamless integration for orchestration of various pipelines, and the visual dashboard that simplifies operations even for non-specialists such as quality analysts."
"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."
"The dashboards in Spring Cloud Dataflow are quite valuable."
 

Cons

"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."
"There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."
"The services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."
"The tool should focus on having an alert system rather than having to use a third-party solution."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"I would improve the dashboard features as they are not very user-friendly."
"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."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
"The solution's community support could be improved."
"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."
"There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or refreshing the dashboard."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
 

Pricing and Cost Advice

"The tool's entry price is cheap. However, pricing increases with data volume."
"The pricing depends on the use cases and the level of usage. If you wanted to use Kinesis for different use cases, there's definitely a cheaper base cost involved. However, it's not entirely cheap, as different use cases might require different levels of Kinesis usage."
"It was actually a fairly high volume we were spending. We were spending about 150 a month."
"The solution's pricing is fair."
"The product falls on a bit of an expensive side."
"In general, cloud services are very convenient to use, even if we have to pay a bit more, as we know what we are paying for and can focus on other tasks."
"The fee is based on the number of hours the service is running."
"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."
"This is an open-source product that can be used free of charge."
"The solution provides value for money, and we are currently using its community edition."
"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."
report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
816,406 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
18%
Manufacturing Company
10%
Retailer
4%
Financial Services Firm
29%
Computer Software Company
17%
Manufacturing Company
7%
Retailer
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
What is your experience regarding pricing and costs for Amazon Kinesis?
I think for us, with Amazon Kinesis, if we have to set up our own Kafka or cluster, it will be very time-consuming. If one considers the aforementioned aspect, Amazon Kinesis is a cheap tool.
What needs improvement with Amazon Kinesis?
There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error. We have to deal with and reduce how many consumers are hitting Amazon Kines...
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...
 

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis
No data available
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
Information Not Available
Find out what your peers are saying about Amazon Kinesis vs. Spring Cloud Data Flow and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.