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."It provides a scalable machine learning library."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"Apache Spark can do large volume interactive data analysis."
"The processing time is very much improved over the data warehouse solution that we were using."
"Provides a lot of good documentation compared to other solutions."
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"This solution provides a clear and convenient syntax for our analytical tasks."
"I feel the streaming is its best feature."
"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."
"Configuring, monitoring, and ensuring observability is a straightforward process."
"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."
"Dynamic DataFrame options are not yet available."
"It's not easy to install."
"The setup I worked on was really complex."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"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."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"The solution needs to optimize shuffling between workers."
"It would be great if we could have a UI-based approach or easily include the specific dependencies we need."
"All the customization and plugins can make the interface too slow and heavy in some situations."
"Jakarta EE's configuration could be simpler, which would make it more useful as a developer experience."
Apache Spark is ranked 2nd in Java Frameworks with 60 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 AWS Fargate, whereas Jakarta EE is most compared with Spring Boot, Spring MVC, Amazon Corretto, Eclipse MicroProfile and Oracle Application Development Framework. See our Apache Spark vs. Jakarta EE report.
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