We performed a comparison between Apache Flink and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. We use Apache Flink to control our clients' installations."
"Apache Flink's best feature is its data streaming tool."
"With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts."
"Allows us to process batch data, stream to real-time and build pipelines."
"Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."
"The setup was not too difficult."
"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"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 product is very user-friendly."
"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."
"Apache Flink should improve its data capability and data migration."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"Apache Flink's documentation should be available in more languages."
"There is room for improvement in the initial setup process."
"The solution could be more user-friendly."
"There is a learning curve. It takes time to learn."
"The machine learning library is not very flexible."
"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
"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."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Spring Cloud Data Flow is ranked 9th in Streaming Analytics with 5 reviews. Apache Flink is rated 7.6, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Apache Flink writes "A great solution with an intricate system and allows for batch data processing". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". Apache Flink is most compared with Amazon Kinesis, Databricks, Azure Stream Analytics, Apache Pulsar and Google Cloud Dataflow, whereas Spring Cloud Data Flow is most compared with Google Cloud Dataflow, Apache Spark Streaming, TIBCO BusinessWorks, Azure Data Factory and Mule Anypoint Platform. See our Apache Flink vs. Spring Cloud Data Flow report.
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