We performed a comparison between Apache Spark and Spring Boot based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Spring Boot has a slight edge in this comparison due to it being the more user-friendly solution. One area where Apache Spark did come out on top was in the ease of deployment category.
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The processing time is very much improved over the data warehouse solution that we were using."
"The solution has been very stable."
"This solution provides a clear and convenient syntax for our analytical tasks."
"There's a lot of functionality."
"The product is useful for analytics."
"The deployment of the product is easy."
"The solution reduces our development time."
"The most valuable feature of Spring Boot is all the interactions to various applications happen using Spring Boot."
"Spring Boot's configuration is easy, and it has an out-of-the-box deployment."
"Spring Boot's main feature is that it's great for DevOps because you can write your own application. You don't need to install Apache Tomcat. You can create your project easily with a few clicks."
"This solution is really user friendly. In terms of prototyping, it's really fast to build the applications we want to test to complete a proof of concept."
"The solution is easy to use; I primarily employ integrated templates such as the REST template."
"The community surrounding Spring Boot is really good. If you face any issue with Spring Boot, you will get the answer from the community."
"It is a stable solution."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Apache Spark provides very good performance The tuning phase is still tricky."
"The setup I worked on was really complex."
"The solution must improve its performance."
"It should support more programming languages."
"Apache Spark should add some resource management improvements to the algorithms."
"It needs more applicable control for large-scale application development."
"communicationbetween different services from the third party layers or with the legacy applications needs to improve."
"The solution could improve its flexibility."
"This solution could be improved if there were more libraries available. We would also like more mobile platform functionality using low levels of code."
"The security could be simplified."
"Spring Boot is okay right now, but my team is looking for some integration where you can make a call to the JMS messaging service and other types of third-party integrations. If the integration with Spring Boot is improved, that would make the tool better. What I'd like to see in the next release of Spring Boot is its integration or tie-up with messaging servers and third-party EFPs, as that would make it very good and more competitive versus other new solutions in the market."
"We'd like to have fewer updates."
"The cloud packaging is not very straightforward."
Apache Spark is ranked 2nd in Java Frameworks with 60 reviews while Spring Boot is ranked 1st in Java Frameworks with 38 reviews. Apache Spark is rated 8.4, while Spring Boot 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 Boot writes "It's highly scalable, secure, and provides all the enhanced tools I need. ". Apache Spark is most compared with AWS Batch, Spark SQL, SAP HANA, Cloudera Distribution for Hadoop and Azure Stream Analytics, whereas Spring Boot is most compared with Jakarta EE, Open Liberty, Eclipse MicroProfile, Vert.x and Oracle Application Development Framework. See our Apache Spark vs. Spring Boot report.
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We monitor all Java Frameworks 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.