We performed a comparison between Azure Data Factory and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution."
"From what we have seen so far, the solution seems very stable."
"Data Factory's most valuable feature is Copy Activity."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"Most of our customers are Microsoft shops and prefer Azure Data Factory because they have good licensing options and a trust factor with Microsoft."
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."
"The trigger scheduling options are decently robust."
"The data copy template is a valuable feature."
"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 most valuable feature is real-time streaming."
"The product is very user-friendly."
"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 could provide more ways to import and export data."
"We require Azure Data Factory to be able to connect to Google Analytics."
"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."
"The thing we missed most was data update, but this is now available as of two weeks ago."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"Some of the optimization techniques are not scalable."
"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."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"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."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Spring Cloud Data Flow is ranked 28th 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 "Provides ease of integration with other cloud platforms ". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, TIBCO BusinessWorks and Mule Anypoint Platform. See our Azure Data Factory vs. Spring Cloud Data Flow report.
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We monitor all Data Integration 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.
