We performed a comparison between Databricks 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 most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."
"We have the ability to scale, collaborate and do machine learning."
"The main features of the solution are efficiency."
"Databricks' most valuable feature is the data transformation through PySpark."
"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."
"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."
"Databricks has helped us have a good presence in data."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"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 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."
"The most valuable feature is real-time streaming."
"This solution only supports queries in SQL and Python, which is a bit limiting."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"Anyone who doesn't know SQL may find the product difficult to work with."
"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."
"Databricks could improve in some of its functionality."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."
"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."
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, Dremio and Microsoft Azure Machine Learning Studio, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, TIBCO BusinessWorks and Confluent. See our Databricks vs. Spring Cloud Data Flow report.
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.