We performed a comparison between Azure Stream Analytics 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's technical support is good."
"It provides the capability to streamline multiple output components."
"We use Azure Stream Analytics for simulation and internal activities."
"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"The life cycle, report management and crash management features are great."
"The solution's most valuable feature is its ability to create a query using SQ."
"It's a product that can scale."
"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."
"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."
"Early in the process, we had some issues with stability."
"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."
"I would like to have a contact individual at Microsoft."
"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."
"Azure Stream Analytics could improve by having clearer metrics as to the scale, more metrics around the data set size that is flowing through it, and performance tuning recommendations."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"The solution's interface could be simpler to understand for non-technical people."
"The initial setup is complex."
"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."
"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."
Azure Stream Analytics is ranked 4th in Streaming Analytics with 22 reviews while Spring Cloud Data Flow is ranked 9th in Streaming Analytics with 5 reviews. Azure Stream Analytics is rated 8.2, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Azure Stream Analytics writes "Easy to set up and user-friendly, but could be priced better". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". Azure Stream Analytics is most compared with Amazon Kinesis, Databricks, Amazon MSK, Apache Flink and Apache Spark, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and TIBCO BusinessWorks.
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.