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Apache Spark vs Spring Boot comparison

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Executive Summary
Updated on May 15, 2022

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.

  • Ease of Deployment: Some Apache Spark users say the initial setup is straightforward, while others feel it is complex. Most Spring Boot users say the initial setup is straightforward.

  • Features: Users of both products are happy with their performance, stability, and scalability. Apache Spark users say it is fast and can handle large amounts of data, but say that its UI should be clearer. Spring Boot users like its monitoring and tracking features but mention integration limitations.
  • Pricing: Both solutions are open-source and are free of charge.
  • Service and Support: Apache Spark and Spring Boot are open-source and therefore do not have dedicated support. However, there are extensive online resources and support forums available for both solutions.

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.

To learn more, read our detailed Apache Spark vs. Spring Boot report (Updated: September 2022).
633,952 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI.""There's a lot of functionality.""The most valuable feature of Apache Spark is its ease of use.""This solution provides a clear and convenient syntax for our analytical tasks.""Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term.""Apache Spark can do large volume interactive data analysis.""One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them.""It is useful for handling large amounts of data. It is very useful for scientific purposes."

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"Spring Boot provides an all-in-one solution for the libraries needed to create a Win app. It covers all the aspects, including validation, security, etc. It provides all those features out-of-the-box. You can do almost everything with Spring Boot.""The setup is straightforward.""Spring Boot could improve its integration with the major cloud providers. Connectivity with cloud solutions isn't easy compared to other frameworks like Django and Python.""The most valuable features of Spring Boot include being able to check all the logs and doing health checks for applications. We can also do monitoring more quickly, and use Spring Boot for production support, so when production goes up or down, we can bring up the application very quickly through Spring Boot.""We like that the product is open-source.""This solution is really user friendly. In terms of prototyping, it's really fast to build the applications we want to test to complete a proof of concept.""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.""This is a stable solution that is being used in the HR space."

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Cons
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn.""The initial setup was not easy.""When you are working with large, complex tasks, the garbage collection process is slow and affects performance.""This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed.""Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases.""It's not easy to install.""We are building our own queries on Spark, and it can be improved in terms of query handling.""Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

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"This is a really good solution for me and I can't think of anything that can be improved.""I would like to see more integration in this solution.""The cloud packaging is not very straightforward.""This solution could be improved if it offered greater integration and was more compatible with other solutions.""Spring Boot could improve the interface, error handling, and integration performance.""It's difficult to explain to junior developers what it does under the hood.""This solution could be improved if there were more libraries available. We would also like more mobile platform functionality using low levels of code.""Spring Boot is okay right now, but my team is looking for some integration where you can make a call to the JMS messaging service and other types of third-party integrations. If the integration with Spring Boot is improved, that would make the tool better. What I'd like to see in the next release of Spring Boot is its integration or tie-up with messaging servers and third-party EFPs, as that would make it very good and more competitive versus other new solutions in the market."

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Pricing and Cost Advice
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • More Apache Spark Pricing and Cost Advice →

  • "It's open-source software, so it's free. It's a community license."
  • "This solution is free unless you apply for support."
  • "As Spring Boot is an open-source tool, it's free."
  • "Spring Boot is an open source solution, it is free to use."
  • More Spring Boot Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Top Answer:It's an open-source product. I don't know much about the licensing aspect.
    Top Answer:1. Open Source 2. Excellent Community Support -- Widely used across different projects -- so your search for answers would be easy and almost certain. 3. Extendable Stack with a wide array of… more »
    Top Answer:Springboot is a Java-based solution that is very popular and easy to use. You can use it to build applications quickly and confidently. Springboot has a very large, helpful learning community, which… more »
    Top Answer:Our organization ran comparison tests to determine whether the Spring Boot or Jakarta EE application creation software was the better fit for us. We decided to go with Spring Boot. Spring Boot offers… more »
    Ranking
    2nd
    out of 11 in Java Frameworks
    Views
    10,662
    Comparisons
    8,468
    Reviews
    12
    Average Words per Review
    436
    Rating
    8.1
    1st
    out of 11 in Java Frameworks
    Views
    19,440
    Comparisons
    14,401
    Reviews
    8
    Average Words per Review
    380
    Rating
    8.5
    Comparisons
    Learn More
    Overview

    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 a tool that makes developing web applications and microservices with the Java Spring Framework faster and easier, with minimal configuration and setup. By using Spring Boot, you avoid all the manual writing of boilerplate code, annotations, and complex XML configurations. Spring Boot integrates easily with other Spring products and can connect with multiple databases.

    How Spring Boot improves Spring Framework

    Java Spring Framework is a popular, open-source framework for creating standalone applications that run on the Java Virtual Machine.

    Although the Spring Framework is powerful, it still takes significant time and knowledge to configure, set up, and deploy Spring applications. Spring Boot is designed to get developers up and running as quickly as possible, with minimal configuration of Spring Framework with three important capabilities.

    • Autoconfiguration: Spring Boot applications are initialized with pre-set dependencies and don't have to be configured manually. Spring Boot also automatically configures both the underlying Spring Framework and any third-party packages based on your settings and on best practices, preventing future errors. Spring Boot's autoconfiguration feature enables you to start developing Spring applications quickly and efficiently. With Spring Boot, you reduce development time and increase the overall efficiency of the development process.

    • Opinionated approach: Spring Boot uses its own judgment for adding and configuring starter packages for your application, depending on the requirements of your project. (These are defined by filling out a simple web-form during the initialization process.) Spring Boot chooses which dependencies to install and which default values to use according to the form’s values.

    • Standalone applications: Spring Boot allows developers to create applications that can run on their own without relying on an external web server, by embedding a web server inside the application. Spring Boot applications can be launched on any platform simply by hitting the Run command.

    Reviews from Real Users

    Spring Boot stands out among its competitors for a number of reasons. Two major ones are its flexible integration options and its autoconfiguration feature, which allows users to start developing applications in a minimal amount of time.

    A system analyst and team lead at a tech services company writes, “Spring Boot has a very lightweight framework, and you can develop projects within a short time. It's open-source and customizable. It's easy to control, has a very interesting deployment policy, and a very interesting testing policy. It's sophisticated. For data analysis and data mining, you can use a custom API and integrate your application. That's an advanced feature. For data managing and other things, you can get that custom from a third-party API. That is also a free license.”

    Randy M., A CEO at Modal Technologies Corporation, writes, “I have found the starter solutions valuable, as well as integration with other products. Spring Security facilitates the handling of standard security measures. The Spring Boot annotations make it easy to handle routing for microservices and to access request and response objects. Other annotations included with Spring Boot enable move away from XML configuration.”

    Offer
    Learn more about Apache Spark
    Learn more about Spring Boot
    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
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    Top Industries
    REVIEWERS
    Computer Software Company29%
    Financial Services Firm29%
    Marketing Services Firm7%
    Non Profit7%
    VISITORS READING REVIEWS
    Financial Services Firm18%
    Computer Software Company18%
    Comms Service Provider15%
    Media Company5%
    REVIEWERS
    Financial Services Firm36%
    Computer Software Company18%
    Security Firm9%
    Government9%
    VISITORS READING REVIEWS
    Comms Service Provider22%
    Computer Software Company16%
    Financial Services Firm16%
    Government6%
    Company Size
    REVIEWERS
    Small Business42%
    Midsize Enterprise23%
    Large Enterprise35%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise12%
    Large Enterprise73%
    REVIEWERS
    Small Business40%
    Midsize Enterprise15%
    Large Enterprise45%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise14%
    Large Enterprise70%
    Buyer's Guide
    Apache Spark vs. Spring Boot
    September 2022
    Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: September 2022.
    633,952 professionals have used our research since 2012.

    Apache Spark is ranked 2nd in Java Frameworks with 13 reviews while Spring Boot is ranked 1st in Java Frameworks with 9 reviews. Apache Spark is rated 8.2, while Spring Boot is rated 8.6. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, the top reviewer of Spring Boot writes "Checks logs and the health of applications; allows quicker monitoring and is also good for production support". Apache Spark is most compared with Azure Stream Analytics, AWS Batch, AWS Lambda, SAP HANA and Apache NiFi, whereas Spring Boot is most compared with Jakarta EE, Eclipse MicroProfile, Open Liberty, 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.