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."Apache Spark can do large volume interactive data analysis."
"The most valuable feature of Apache Spark is its ease of use."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"We use Spark to process data from different data sources."
"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."
"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."
"Jakarta EE's best features include REST services, configuration, and persistent facilities. It's also incredibly cloud friendly."
"The feature that allows a variation of work space based on the application being used."
"Configuring, monitoring, and ensuring observability is a straightforward process."
"The product could improve the user interface and make it easier for new users."
"The migration of data between different versions could be improved."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"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."
"The setup I worked on was really complex."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"It's not easy to install."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"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."
"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 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.
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