We performed a comparison between Apache Spark and Spring MVC based on real PeerSpot user reviews.
Find out in this report how the two Java Frameworks solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The most valuable feature of Apache Spark is its flexibility."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"ETL and streaming capabilities."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The product's deployment phase is easy."
"There's a lot of functionality."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"When we shifted from our legacy frameworks to the Spring framework, we discovered that Spring definitely made our development easier. One good example is that there is a lot of boiler plate code available that you don't have to write from scratch, making the development of web applications a much simpler process."
"Spring MVC is fast and reliable."
"The most valuable feature is simplicity."
"The most valuable features of Spring MVC are the modules, such as Spring Admin. All the Spring solutions work well together and are simple to maintain, such as the load balancing on the client side."
"The best feature of Spring MVC is its auto-configuration capabilities."
"The most valuable feature of Spring MVC is the configuration, such as WAF."
"The solution can scale."
"Spring gives you the opportunity to develop architecture in the simplest way possible. It comes with everything you would want in terms of security. If you want to access the database, you have the ability to do that."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"There were some problems related to the product's compatibility with a few Python libraries."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"I saw some error messages coming up when they were getting problems actually viewing all the reports."
"I have recently had problems with the changes that were made using Spring Security."
"Spring IDE needs some work and improvement. We have faced many issues when adding third-party Eclipse plugins."
"We would like the deployment of this solution to be easier as, at present, it is quite complicated."
"It could provide faster performance."
"The documentation for Spring MVC could improve."
"The newer versions of Spring MVC have released a lot of features that we are not using right now because, in many cases, we are limited to running older versions. As such, it would be nice if Spring were to improve support for upgrading to newer versions, especially for legacy applications."
"The initial setup could be more straightforward."
Apache Spark is ranked 2nd in Java Frameworks with 60 reviews while Spring MVC is ranked 3rd in Java Frameworks with 16 reviews. Apache Spark is rated 8.4, while Spring MVC is rated 8.4. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of Spring MVC writes "Straightforward setup, highly stable, and useful online support". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas Spring MVC is most compared with Jakarta EE, Spring Boot, Open Liberty, Oracle Application Development Framework and Vert.x. See our Apache Spark vs. Spring MVC report.
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