We performed a comparison between Apache Spark and Jakarta EE 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 solution has been very stable."
"The most valuable feature of Apache Spark is its flexibility."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"This solution provides a clear and convenient syntax for our analytical tasks."
"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."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"I feel the streaming is its best feature."
"Configuring, monitoring, and ensuring observability is a straightforward process."
"The feature that allows a variation of work space based on the application being used."
"Jakarta EE's best features include REST services, configuration, and persistent facilities. It's also incredibly cloud friendly."
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."
"The migration of data between different versions could be improved."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"Apache Spark provides very good performance The tuning phase is still tricky."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"The solution needs to optimize shuffling between workers."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"Apache Spark should add some resource management improvements to the algorithms."
"Jakarta EE's configuration could be simpler, which would make it more useful as a developer experience."
"All the customization and plugins can make the interface too slow and heavy in some situations."
"It would be great if we could have a UI-based approach or easily include the specific dependencies we need."
Apache Spark is ranked 2nd in Java Frameworks with 58 reviews while Jakarta EE is ranked 4th in Java Frameworks with 3 reviews. Apache Spark is rated 8.4, while Jakarta EE is rated 7.4. 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 Jakarta EE writes "A robust enterprise Java capabilities with complex configuration involved, making it a powerful choice for scalable applications while requiring a learning curve". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Amazon Corretto, whereas Jakarta EE is most compared with Spring Boot, Spring MVC, Amazon Corretto, Eclipse MicroProfile and Vert.x. See our Apache Spark vs. Jakarta EE report.
See our list of best Java Frameworks vendors.
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.