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."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"ETL and streaming capabilities."
"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"Spring Boot facilitates the use of Java which is open source. We use Github and other libraries that are available which assist in the building we need to do."
"The most valuable feature of Spring Boot is the microservices and change information. Additionally, there are plenty of features."
"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."
"Features that help with monitoring and tracking network calls between several micro services."
"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 setup is straightforward."
"This is a pretty light solution. It's not too heavy."
"This is a stable solution that is being used in the HR space."
"The product could improve the user interface and make it easier for new users."
"One limitation is that not all machine learning libraries and models support it."
"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."
"The migration of data between different versions could be improved."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"Dynamic DataFrame options are not yet available."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"Building a new product in Spring Boot can take a long time since the solution uses reflection. This is one area the solution could be improved."
"The solution has some vulnerabilities and fails our security audits, forcing us to keep fixing the solution."
"We'd like to have fewer updates."
"They should include tutorial videos for learning new features."
"Nothing really comes to mind in terms of areas of improvement."
"They should integrate the solution with more AI and machine learning platforms."
"The services we develop are purely synchronous services, so there's a blocking and waiting state. This is a big problem in microservices."
"The security could be simplified."
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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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.
