Compare Apache Spark vs. Spring Boot

Apache Spark is ranked 1st in Java Frameworks with 6 reviews while Spring Boot is ranked 3rd in Java Frameworks with 1 review. Apache Spark is rated 7.8, while Spring Boot is rated 5.0. The top reviewer of Apache Spark writes "Fast performance and has an easy initial setup". On the other hand, the top reviewer of Spring Boot writes "Makes it difficult to support a specific functionality in a user-friendly manner, but simplifies application deployment". Apache Spark is most compared with Spring Boot, AWS Lambda and Azure Stream Analytics, whereas Spring Boot is most compared with Apache Spark, Oracle Application Development Framework and Spring MVC.
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11,333 views|9,306 comparisons
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2,054 views|1,710 comparisons
Most Helpful Review
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We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

I found the solution stable. We haven't had any problems with it.The scalability has been the most valuable aspect of the solution.Features include machine learning, real time streaming, and data processing.The fault tolerant feature is provided.It provides a scalable machine learning library.With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware.

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Spring Boot is much easier when it comes to the configuration, setup, installation, and deployment of your applications, compared to any kind of MVC framework. It has everything within a single framework.

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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 management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive.It should support more programming languages.Needs to provide an internal schedule to schedule spark jobs with monitoring capability.Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing.

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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.

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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 designed to get developers up and running as quickly as possible, with minimal upfront configuration of Spring. Spring Boot takes an opinionated view of building production-ready applications. Make implementing modern application best practices an intuitive and easy first practice! Build microservices with REST, WebSocket, Messaging, Reactive, Data, Integration, and Batch capabilities via a simple and consistent development experience.

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Sample Customers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab,, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
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Top Industries
Financial Services Firm29%
Software R&D Company29%
Marketing Services Firm14%
Healthcare Company14%
Software R&D Company27%
Comms Service Provider12%
Financial Services Firm11%
Media Company9%
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Find out what your peers are saying about Apache Spark vs. Spring MVC and other solutions. Updated: September 2019.
370,655 professionals have used our research since 2012.
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.
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