We performed a comparison between Apache Spark and Spring Boot based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Spring Boot has a slight edge in this comparison due to it being the more user-friendly solution. One area where Apache Spark did come out on top was in the ease of deployment category.
"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."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"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 is very stable."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The product's deployment phase is easy."
"The most valuable feature of Apache Spark is its ease of use."
"Spark can handle small to huge data and is suitable for any size of company."
"It is a stable solution. Stability-wise, I rate the solution a nine out of ten...The initial setup was not complex and was a simple process."
"The setup is straightforward."
"The most valuable feature of Spring Boot is it reduces the configuration needed. The configuration is handled by the solution. For example, if you're going to develop a web service, we needed to have a Tomcat web server and had to deploy the services and do tests. However, with Spring Boot, the default server comes with Spring Boot which reduces the task of doing all the configuration."
"The Spring Cloud Gateway, Load Balancer are the valuable features. Apart from them, handling a sync call, then multiple service communication through field clients are also useful features."
"The cloud version is very scalable."
"It's easy to set up the solution."
"The simplicity is excellent."
"I have found the starter solutions valuable, as well as integration with other products."
"The logging for the observability platform could be better."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"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 initial setup was not easy."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."
"The database connectivity could be better in terms of dealing with multi-tenant systems."
"The security could be simplified."
"It needs to be simplified, more user-friendly."
"We'd like them to develop more supporting testing."
"If you want to have multiple integrations, the setup phase will become complex."
"Spring Boot's cost could be cheaper."
"The cross framework compatibility has some shortcomings. With JUnit Test Runner and Spring Boot, it's really tedious to make them both work to write the test cases."
"Perhaps an even lighter-weight, leaner version could be made available, to compete with alternative solutions, such as NodeJS."
Apache Spark is ranked 2nd in Java Frameworks with 60 reviews while Spring Boot is ranked 1st in Java Frameworks with 38 reviews. Apache Spark is rated 8.4, while Spring Boot is rated 8.4. 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 Spring Boot writes "It's highly scalable, secure, and provides all the enhanced tools I need. ". Apache Spark is most compared with AWS Batch, Spark SQL, SAP HANA, Cloudera Distribution for Hadoop and AWS Lambda, whereas Spring Boot is most compared with Jakarta EE, Open Liberty, Eclipse MicroProfile, Vert.x and Oracle Application Development Framework. See our Apache Spark vs. Spring Boot report.
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