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Apache Spark vs Spring Boot comparison

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9,844 views|7,892 comparisons
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14,313 views|11,473 comparisons
Featured Review
Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: January 2022.
564,643 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The features we find most valuable are the machine learning, data learning, and Spark Analytics.""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 memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly.""Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark.""The solution has been very stable.""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.""The main feature that we find valuable is that it is very fast.""Apache Spark can do large volume interactive data analysis."

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"Features that help with monitoring and tracking network calls between several micro services.""I have found the starter solutions valuable, as well as integration with other products.""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.""The platform is easy for developers to download.""It gives you confidence in a readily available platform.""The cloud version is very scalable."

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Cons
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing.""I would like to see integration with data science platforms to optimize the processing capability for these tasks.""We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.""It's not easy to install.""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.""Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available.""We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.""The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."

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"It needs to be simplified, more user-friendly.""communicationbetween different services from the third party layers or with the legacy applications needs to improve.""Perhaps an even lighter-weight, leaner version could be made available, to compete with alternative solutions, such as NodeJS.""The security could be simplified.""I would like to see more integration in this solution.""Having to restart the application to reload properties."

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Pricing and Cost Advice
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "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."
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  • "Spring Boot is free; even the Spring Tools Suite for Eclipse is free."
  • "This is an open-source product."
  • "It's open-source software, so it's free. It's a community license."
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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: 
    The solution has been very stable.
    Top Answer: 
    We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud.
    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
    9,844
    Comparisons
    7,892
    Reviews
    8
    Average Words per Review
    457
    Rating
    8.8
    1st
    out of 11 in Java Frameworks
    Views
    14,313
    Comparisons
    11,473
    Reviews
    6
    Average Words per Review
    586
    Rating
    8.7
    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 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, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
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    Top Industries
    REVIEWERS
    Financial Services Firm40%
    Computer Software Company20%
    Marketing Services Firm10%
    Non Profit10%
    VISITORS READING REVIEWS
    Computer Software Company24%
    Comms Service Provider20%
    Financial Services Firm11%
    Media Company8%
    VISITORS READING REVIEWS
    Comms Service Provider29%
    Computer Software Company22%
    Financial Services Firm11%
    Government6%
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise20%
    Large Enterprise40%
    REVIEWERS
    Small Business60%
    Midsize Enterprise10%
    Large Enterprise30%
    Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: January 2022.
    564,643 professionals have used our research since 2012.

    Apache Spark is ranked 2nd in Java Frameworks with 9 reviews while Spring Boot is ranked 1st in Java Frameworks with 6 reviews. Apache Spark is rated 8.4, 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 "Good security and integration, and the autowiring feature saves on development time". Apache Spark is most compared with Azure Stream Analytics, AWS Lambda, SAP HANA, AWS Batch and Cloudera Distribution for Hadoop, whereas Spring Boot is most compared with Jakarta EE, Eclipse MicroProfile, Open Liberty, Oracle Application Development Framework and Vert.x. See our Apache Spark vs. Spring Boot report.

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