We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"The solution is very stable."
"I feel the streaming is its best feature."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"The main feature that we find valuable is that it is very fast."
"The processing time is very much improved over the data warehouse solution that we were using."
"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."
"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."
"It's great because it simplifies development. Together with MyBatis they make a beautiful pair for Java development."
"I have found the starter solutions valuable, as well as integration with other products."
"Features that help with monitoring and tracking network calls between several micro services."
"It gives you confidence in a readily available platform."
"The cloud version is very scalable."
"The platform is easy for developers to download."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"The solution needs to optimize shuffling between workers."
"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."
"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."
"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."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"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."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The product could be improved by supporting and integrating Hadoop."
"Perhaps an even lighter-weight, leaner version could be made available, to compete with alternative solutions, such as NodeJS."
"Having to restart the application to reload properties."
"communicationbetween different services from the third party layers or with the legacy applications needs to improve."
"The security could be simplified."
"It needs to be simplified, more user-friendly."
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 1st in Java Frameworks with 10 reviews while Spring Boot is ranked 2nd in Java Frameworks with 6 reviews. Apache Spark is rated 8.6, while Spring Boot is rated 8.6. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". 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 Batch, SAP HANA, AWS Lambda and Apache NiFi, whereas Spring Boot is most compared with Eclipse MicroProfile, Jakarta EE, Open Liberty, 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.