Compare Apache Spark vs. Spring Boot

Apache Spark is ranked 1st in Java Frameworks with 8 reviews while Spring Boot is ranked 2nd in Java Frameworks with 2 reviews. Apache Spark is rated 7.8, while Spring Boot is rated 7.0. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". 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, Azure Stream Analytics and AWS Lambda, whereas Spring Boot is most compared with Apache Spark, Oracle Application Development Framework and Spring MVC. See our Apache Spark vs. Spring Boot report.
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10,927 views|9,123 comparisons
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2,549 views|2,072 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: January 2020.
389,772 professionals have used our research since 2012.
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We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

I feel the streaming is its best feature.The solution is very stable.The most valuable feature of this solution is its capacity for processing large amounts of data.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.

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It's great because it simplifies development. Together with MyBatis they make a beautiful pair for Java development.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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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.The solution needs to optimize shuffling between workers.When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.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.

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The product could be improved by supporting and integrating Hadoop.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
Software R&D Company29%
Financial Services Firm29%
Non Profit14%
Marketing Services Firm14%
Software R&D Company32%
Comms Service Provider13%
Financial Services Firm10%
Media Company8%
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Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: January 2020.
389,772 professionals have used our research since 2012.
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