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 most valuable feature of Apache Spark is its ease of use."
"The most valuable feature of Apache Spark is its flexibility."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"The main feature that we find valuable is that it is very fast."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The processing time is very much improved over the data warehouse solution that we were using."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The data processing framework is good."
"We like that the product is open-source."
"It gives you confidence in a readily available platform."
"The API gateway and cloud configuration allows us to configure the properties outside of the service with respect to enrollment."
"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 configuration setup in Spring Boot is pretty simplified compared to Hibernate ORM."
"This is a stable solution that is being used in the HR space."
"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."
"It is a very scalable solution."
"The solution must improve its performance."
"Apache Spark should add some resource management improvements to the algorithms."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"One limitation is that not all machine learning libraries and models support it."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"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."
"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"The product could improve the user interface and make it easier for new users."
"The solution could improve its flexibility."
"They should include tutorial videos for learning new features."
"We'd like to have fewer updates."
"The services we develop are purely synchronous services, so there's a blocking and waiting state. This is a big problem in microservices."
"When we change versions, we run into issues."
"The product could be improved by supporting and integrating Hadoop."
"I would like to see more integration in this solution."
"This solution could be improved if there were more libraries available. We would also like more mobile platform functionality using low levels of code."
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.
