We performed a comparison between Apache Spark Streaming and Spring Cloud Data Flow based on real PeerSpot user reviews.Find out what your peers are saying about Databricks, Microsoft, Confluent and others in Streaming Analytics.
"The solution is better than average and some of the valuable features include efficiency and stability."
"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."
"The solution is very stable and reliable."
"As an open-source solution, using it is basically free."
"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."
"We would like to have the ability to do arbitrary stateful functions in Python."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"The solution itself could be easier to use."
"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."
"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."
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.
Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.
Apache Spark Streaming is ranked 10th in Streaming Analytics with 4 reviews while Spring Cloud Data Flow is ranked 7th in Streaming Analytics with 1 review. Apache Spark Streaming is rated 7.8, while Spring Cloud Data Flow is rated 7.0. The top reviewer of Apache Spark Streaming writes "Mature and stable with good scalability". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Simple programming model, low maintenance, but interface could improve". Apache Spark Streaming is most compared with Amazon Kinesis, Apache Flink, Azure Stream Analytics, Confluent and Databricks, whereas Spring Cloud Data Flow is most compared with Apache Flink, Amazon Kinesis, Databricks, Mule Anypoint Platform and Cloudera DataFlow.
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