"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."
"The solution has been very stable."
"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."
"The main feature that we find valuable is that it is very fast."
"Apache Spark can do large volume interactive data analysis."
"Features that help with monitoring and tracking network calls between several micro services."
"I have found the starter solutions valuable, as well as integration with other products."
"Spring Boot has a very lightweight framework, and you can develop projects within a short time. It's open-source and customizable. It's easy to control, has a very interesting deployment policy, and a very interesting testing policy. It's sophisticated."
"The platform is easy for developers to download."
"It gives you confidence in a readily available platform."
"The cloud version is very scalable."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."
"It's not easy to install."
"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."
"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."
"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."
"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."
"It needs to be simplified, more user-friendly."
"communicationbetween different services from the third party layers or with the legacy applications needs to improve."
"Perhaps an even lighter-weight, leaner version could be made available, to compete with alternative solutions, such as NodeJS."
"The security could be simplified."
"I would like to see more integration in this solution."
"Having to restart the application to reload properties."
Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
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Apache Spark is ranked 2nd in Java Frameworks with 9 reviews while Spring Boot is ranked 1st in Java Frameworks with 6 reviews. Apache Spark is rated 8.4, while Spring Boot is rated 8.6. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, the top reviewer of Spring Boot writes "Good security and integration, and the autowiring feature saves on development time". Apache Spark is most compared with Azure Stream Analytics, AWS Lambda, SAP HANA, AWS Batch and Cloudera Distribution for Hadoop, whereas Spring Boot is most compared with Jakarta EE, Eclipse MicroProfile, Open Liberty, Oracle Application Development Framework and Vert.x. See our Apache Spark vs. Spring Boot report.
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