We performed a comparison between Azure Data Factory and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Informatica, Oracle and others in Data Integration."The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"In terms of my personal experience, it works fine."
"We have been using drivers to connect to various data sets and consume data."
"It is beneficial that the solution is written with Spark as the back end."
"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."
"Its integrability with the rest of the activities on Azure is most valuable."
"The most valuable feature is real-time streaming."
"The product is very user-friendly."
"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."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"There aren't many third-party extensions or plugins available in the solution."
"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."
"It would be better if it had machine learning capabilities."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"The thing we missed most was data update, but this is now available as of two weeks ago."
"Azure Data Factory's pricing in terms of utilization could be improved."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
"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."
Azure Data Factory is ranked 1st in Data Integration with 79 reviews while Spring Cloud Data Flow is ranked 30th in Data Integration with 5 reviews. Azure Data Factory is rated 8.0, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Good logging mechanisms, a strong infrastructure and pretty scalable". Azure Data Factory is most compared with Informatica PowerCenter, Alteryx Designer, Informatica Cloud Data Integration, Snowflake and Microsoft Azure Synapse Analytics, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Mule Anypoint Platform and TIBCO BusinessWorks.
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