We performed a comparison between Databricks and Spring Cloud Data Flow based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), Databricks, Microsoft and others in Streaming Analytics."The setup was straightforward."
"Databricks is a scalable solution. It is the largest advantage of the solution."
"The solution is very simple and stable."
"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"Easy to use and requires minimal coding and customizations."
"There are good features for turning off clusters."
"Automation with Databricks is very easy when using the API."
"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 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 product is very user-friendly."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"The product should provide more advanced features in future releases."
"The integration of data could be a bit better."
"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."
"It would be very helpful if Databricks could integrate with platforms in addition to Azure."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"There is room for improvement in the documentation of processes and how it works."
"There is room for improvement in visualization."
"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."
"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."
Databricks is ranked 2nd in Streaming Analytics with 78 reviews while Spring Cloud Data Flow is ranked 9th in Streaming Analytics with 5 reviews. Databricks is rated 8.2, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and Informatica PowerCenter.
See our list of best Streaming Analytics vendors.
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.