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 crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."
"This solution provides a clear and convenient syntax for our analytical tasks."
"The solution is very stable."
"The deployment of the product is easy."
"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."
"The data processing framework is good."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"Apache Spark can do large volume interactive data analysis."
"The solution is easy to use; I primarily employ integrated templates such as the REST template."
"It is a very scalable solution."
"The cloud version is very scalable."
"It's very easy to get started. It's very quick. Most of the configurations are already available. So not much time is spent on setting up things. One can quickly set up and then get rolling."
"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 setup is straightforward."
"I have found the starter solutions valuable, as well as integration with other products."
"The API gateway and cloud configuration allows us to configure the properties outside of the service with respect to enrollment."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"The initial setup was not easy."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"Apache Spark should add some resource management improvements to the algorithms."
"Apache Spark provides very good performance The tuning phase is still tricky."
"The product could be improved by supporting and integrating Hadoop."
"Spring Boot can improve the dependency tree that we use for libraries. It would be helpful if it was less complex."
"The solution has some vulnerabilities and fails our security audits, forcing us to keep fixing the solution."
"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."
"I would like to see more integration in this solution."
"communicationbetween different services from the third party layers or with the legacy applications needs to improve."
"Having to restart the application to reload properties."
"When we change versions, we run into issues."
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.
