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 solution is very stable."
"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."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."
"The fault tolerant feature is provided."
"The solution is scalable."
"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 solution reduces our development time."
"We like that the product is open-source."
"Spring Boot's main feature is that it's great for DevOps because you can write your own application. You don't need to install Apache Tomcat. You can create your project easily with a few clicks."
"The setup is straightforward."
"The solution's framework is stable."
"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."
"It is stable."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"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."
"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"One limitation is that not all machine learning libraries and models support it."
"Dynamic DataFrame options are not yet available."
"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."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"At the initial stage, the product provides no container logs to check the activity."
"When we change versions, we run into issues."
"It needs more applicable control for large-scale application development."
"I would like to see more integration in this solution."
"The current state of Spring Boot's cloud layer requires further development, especially for collecting Java functions for cloud platforms like GCP Cloudground. Having to write every single API request in a single class can be a cumbersome and time-consuming task that is not ideal for Java developers. Additionally, having all API calls in one class and making it the main class presents problems with package visibility. Therefore, there is much room for improvement in the Spring Cloud area."
"We have specific algorithms for our Load Balancer or API gateway. So those things, if they could make it more precise, that would be beneficial. Sometimes when we are under pressure or any new person who looks into that stuff, we'll get confused or scared because of some difficulties in understanding Which algorithm needs to be used to implement a Load Balancer. When when we Yeah. Because when we say circuit breaker, we need to use it, and then the user gets a blank circuit breaker. This means we are saying the circuit breaker needs to be moved, and then that circuit breaker needs to be elaborated more. What type of algorithm should I do, and what exactly do I need to get done so that this circuit breaker can help me to resolve my issue? Because, you know, because if you go for the circuit breaker, it will ask to open the new tab, you know, since it will check. If the service is not responding, it will wait and go for another connection. So in similar words, if they can explain it a bit more, that will be helpful. Everyone could do their own Google stuff, and they will get it, but they need help understanding how this could help them to resolve the issue. It will be good if Spring Boot provides information about real-time use cases."
"The solution has some vulnerabilities and fails our security audits, forcing us to keep fixing the solution."
"This solution could be improved if it offered greater integration and was more compatible with other solutions."
"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 Azure Stream Analytics, 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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