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 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."
"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."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"The data processing framework is good."
"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."
"I found the solution stable. We haven't had any problems with it."
"The solution reduces our development time."
"I have found the starter solutions valuable, as well as integration with other products."
"Features that help with monitoring and tracking network calls between several micro services."
"The cloud version is very scalable."
"It gives you confidence in a readily available platform."
"The most valuable feature of Spring Boot is all the interactions to various applications happen using Spring Boot."
"The simplicity is excellent."
"It's easy to set up the solution."
"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."
"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."
"They could improve the issues related to programming language for the platform."
"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"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."
"The solution needs to optimize shuffling between workers."
"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."
"This solution could be improved if there were more libraries available. We would also like more mobile platform functionality using low levels of code."
"The security could be simplified."
"We'd like to have fewer updates."
"The product could be improved by supporting and integrating Hadoop."
"If you want to have multiple integrations, the setup phase will become complex."
"Perhaps an even lighter-weight, leaner version could be made available, to compete with alternative solutions, such as NodeJS."
"If you want to create large microservices applications, you need to connect several applications and services to each other. It is very complicated, and Spring Boot does not have an integrated solution for it."
"Spring Boot is lacking visibility in terms of how that business process or business rule would look within your application. Because everything has been embedded within the code itself, it disables the visibility. the ability to maintain or even support a specific functionality in a user-friendly manner, where a developer can come up and just adjust that part of that process."
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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