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.
"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."
"There's a lot of functionality."
"The most valuable feature of Apache Spark is its ease of use."
"This solution provides a clear and convenient syntax for our analytical tasks."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"Apache Spark can do large volume interactive data analysis."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"Spring Boot provides an all-in-one solution for the libraries needed to create a Win app. It covers all the aspects, including validation, security, etc. It provides all those features out-of-the-box. You can do almost everything with Spring Boot."
"The setup is straightforward."
"Spring Boot could improve its integration with the major cloud providers. Connectivity with cloud solutions isn't easy compared to other frameworks like Django and Python."
"The most valuable features of Spring Boot include being able to check all the logs and doing health checks for applications. We can also do monitoring more quickly, and use Spring Boot for production support, so when production goes up or down, we can bring up the application very quickly through Spring Boot."
"We like that the product is open-source."
"This solution is really user friendly. In terms of prototyping, it's really fast to build the applications we want to test to complete a proof of concept."
"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."
"This is a stable solution that is being used in the HR space."
"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 initial setup was not easy."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."
"It's not easy to install."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"This is a really good solution for me and I can't think of anything that can be improved."
"I would like to see more integration in this solution."
"The cloud packaging is not very straightforward."
"This solution could be improved if it offered greater integration and was more compatible with other solutions."
"Spring Boot could improve the interface, error handling, and integration performance."
"It's difficult to explain to junior developers what it does under the hood."
"This solution could be improved if there were more libraries available. We would also like more mobile platform functionality using low levels of code."
"Spring Boot is okay right now, but my team is looking for some integration where you can make a call to the JMS messaging service and other types of third-party integrations. If the integration with Spring Boot is improved, that would make the tool better. What I'd like to see in the next release of Spring Boot is its integration or tie-up with messaging servers and third-party EFPs, as that would make it very good and more competitive versus other new solutions in the market."
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
Spring Boot is a tool that makes developing web applications and microservices with the Java Spring Framework faster and easier, with minimal configuration and setup. By using Spring Boot, you avoid all the manual writing of boilerplate code, annotations, and complex XML configurations. Spring Boot integrates easily with other Spring products and can connect with multiple databases.
How Spring Boot improves Spring Framework
Java Spring Framework is a popular, open-source framework for creating standalone applications that run on the Java Virtual Machine.
Although the Spring Framework is powerful, it still takes significant time and knowledge to configure, set up, and deploy Spring applications. Spring Boot is designed to get developers up and running as quickly as possible, with minimal configuration of Spring Framework with three important capabilities.
Reviews from Real Users
Spring Boot stands out among its competitors for a number of reasons. Two major ones are its flexible integration options and its autoconfiguration feature, which allows users to start developing applications in a minimal amount of time.
A system analyst and team lead at a tech services company writes, “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. For data analysis and data mining, you can use a custom API and integrate your application. That's an advanced feature. For data managing and other things, you can get that custom from a third-party API. That is also a free license.”
Randy M., A CEO at Modal Technologies Corporation, writes, “I have found the starter solutions valuable, as well as integration with other products. Spring Security facilitates the handling of standard security measures. The Spring Boot annotations make it easy to handle routing for microservices and to access request and response objects. Other annotations included with Spring Boot enable move away from XML configuration.”
Apache Spark is ranked 2nd in Java Frameworks with 13 reviews while Spring Boot is ranked 1st in Java Frameworks with 9 reviews. Apache Spark is rated 8.2, 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 "Checks logs and the health of applications; allows quicker monitoring and is also good for production support". Apache Spark is most compared with Azure Stream Analytics, AWS Batch, AWS Lambda, SAP HANA and Apache NiFi, whereas Spring Boot is most compared with Jakarta EE, Eclipse MicroProfile, 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.