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Spring Cloud Data Flow Reviews

Name: Spring Cloud Data Flow
Brand: VMware
Rating: 3.9 (9 reviews)

Vendor: VMware

3.9 out of 5

9 reviews
88% willing to recommend

131 followers

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What is Spring Cloud Data Flow?

Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.

Get the Spring Cloud Data Flow Buyer's Guide and find out what your peers are saying about Spring Cloud Data Flow, Informatica Intelligent Data Management Cloud (IDMC), Azure Data Factory and more!

Spring Cloud Data Flow is the #10 ranked solution in Streaming Analytics tools and #22 ranked solution in top Data Integration Tools. PeerSpot users give Spring Cloud Data Flow an average rating of 7.8 out of 10. Based on the analysis of the 9 most recent Spring Cloud Data Flow reviews, the overall sentiment is positive, with a sentiment score of 6.7. Spring Cloud Data Flow is most commonly compared to Informatica Intelligent Data Management Cloud (IDMC): Spring Cloud Data Flow vs Informatica Intelligent Data Management Cloud (IDMC). Spring Cloud Data Flow is popular among the large enterprise segment, accounting for 78% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 29% of all views.

Buyer's Guide

Spring Cloud Data Flow

October 2024

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Helped 816,406 peers since 2012

Featured reviews

NitinGoyal

Engineering Lead at Naukri.com

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.

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Kaleeswaran Karuppusamy

Cloud Application Architect at Lumen Technologies

There are no stability issues in the tool. Compared to Apache Sling, Spring Cloud Data Flow is not easy to use. People prefer Apache Sling when dealing with their use cases. I don't know whether Spring Cloud Data Flow will be demanded a lot in the market because there are a lot of other options available, especially open-source tools like Apache Sling, which is getting a lot of attention from people. A lot of people have started using Apache Sling, so I don't know how much more visible Spring Cloud Data Flow will be in the future.

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Alokik Gupta

Software Engineer at Tech Mahindra Limited

The dashboards in Spring Cloud Dataflow are quite valuable. By injecting the dependency of Spring Cloud Dataflow into our Spring Boot application and annotating it with 'enable task annotation', we can manage tasks effectively. Additionally, the platform allows us to create pipelines and use microservices like a logical AND gate, giving us greater control over our microservices.

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Spring Cloud Data Flow mindshare

Product category:

As of November 2024, the mindshare of Spring Cloud Data Flow in the Data Integration category stands at 1.1%, up from 1.0% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Integration

Key learnings from peers

Last updated Nov 21, 2024

Valuable Features

"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 dashboards in Spring Cloud Dataflow are quite valuable."
"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."

Room for Improvement

"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."
"I would improve the dashboard features as they are not very user-friendly."
"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."

Pricing

"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."
"This is an open-source product that can be used free of charge."

Popular Use Cases

Deployment

Scalability

Spring Cloud Data Flow excels in scalability, allowing for horizontal expansion and multiple instances across various servers. Users benefit from integrating with platforms such as PCF that support seamless scaling. Companies utilize its capabilities by deploying multiple microservices, enhancing flexibility and capacity as demands grow. Currently, small teams use it effectively, with plans to expand its usage in the future.