We performed a comparison between Google Cloud Dataflow and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics."The solution allows us to program in any language we desire."
"The support team is good and it's easy to use."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"It is a scalable solution."
"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."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The service is relatively cheap compared to other batch-processing engines."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"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."
"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 most valuable feature is real-time streaming."
"The product is very user-friendly."
"The technical support has slight room for improvement."
"The solution's setup process could be more accessible."
"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."
"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."
"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."
"Google Cloud Dataflow should include a little cost optimization."
"The deployment time could also be reduced."
"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."
"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."
Google Cloud Dataflow is ranked 7th in Streaming Analytics with 10 reviews while Spring Cloud Data Flow is ranked 9th in Streaming Analytics with 5 reviews. Google Cloud Dataflow is rated 7.8, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Google Cloud Dataflow writes "Easy to use for programmers, user-friendly, and scalable". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". Google Cloud Dataflow is most compared with Databricks, Apache NiFi, Amazon MSK, Amazon Kinesis and Apache Flink, whereas Spring Cloud Data Flow is most compared with Apache Flink, Apache Spark Streaming, Azure Data Factory, TIBCO BusinessWorks and Mule Anypoint Platform.
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We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
