Apache Spark vs Jakarta EE comparison

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Executive Summary

We performed a comparison between Apache Spark and Jakarta EE based on real PeerSpot user reviews.

Find out in this report how the two Java Frameworks solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Apache Spark vs. Jakarta EE Report (Updated: March 2024).
765,234 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
"The solution has been very stable.""The most valuable feature of Apache Spark is its flexibility.""The features we find most valuable are the machine learning, data learning, and Spark Analytics.""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.""This solution provides a clear and convenient syntax for our analytical tasks.""I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten.""It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained.""I feel the streaming is its best feature."

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"Configuring, monitoring, and ensuring observability is a straightforward process.""The feature that allows a variation of work space based on the application being used.""Jakarta EE's best features include REST services, configuration, and persistent facilities. It's also incredibly cloud friendly."

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Cons
"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing.""The migration of data between different versions could be improved.""It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster.""Apache Spark provides very good performance The tuning phase is still tricky.""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.""The solution needs to optimize shuffling between workers.""Spark could be improved by adding support for other open-source storage layers than Delta Lake.""Apache Spark should add some resource management improvements to the algorithms."

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"Jakarta EE's configuration could be simpler, which would make it more useful as a developer experience.""All the customization and plugins can make the interface too slow and heavy in some situations.""It would be great if we could have a UI-based approach or easily include the specific dependencies we need."

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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."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "We are using the free version of the solution."
  • "Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
  • "Apache Spark is an expensive solution."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
  • "It is an open-source solution, it is free of charge."
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  • "I would rate Jakarta EE's pricing seven out of ten."
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    Questions from the Community
    Top Answer:The product’s most valuable features are lazy evaluation and workload distribution.
    Top Answer:They provide an open-source license for the on-premise version. However, we have to pay for the cloud version including data centers and virtual machines.
    Top Answer:They could improve the issues related to programming language for the platform.
    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 »
    Top Answer:Configuring, monitoring, and ensuring observability is a straightforward process.
    Top Answer:Enhancements in configurations can be achieved by benchmarking against Spring Boot technology. It would be great if we could have a UI-based approach or easily include the specific dependencies we… more »
    Ranking
    2nd
    out of 12 in Java Frameworks
    Views
    4,443
    Comparisons
    3,443
    Reviews
    20
    Average Words per Review
    387
    Rating
    8.6
    4th
    out of 12 in Java Frameworks
    Views
    10,538
    Comparisons
    9,091
    Reviews
    2
    Average Words per Review
    323
    Rating
    6.5
    Comparisons
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    Spark SQL logo
    Compared 10% of the time.
    SAP HANA logo
    Compared 7% of the time.
    Amazon Corretto logo
    Compared 2% of the time.
    Spring Boot logo
    Compared 84% of the time.
    Spring MVC logo
    Compared 6% of the time.
    Amazon Corretto logo
    Compared 4% of the time.
    Eclipse MicroProfile logo
    Compared 3% of the time.
    Vert.x logo
    Compared 1% of the time.
    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

    Jakarta EE is a powerful platform for developing enterprise-level Java applications. It provides a set of specifications and APIs that enable developers to build scalable, secure, and portable applications. Jakarta EE is built on the foundation of Java EE, which has been widely adopted by organizations around the world.

    One of the key features of Jakarta EE is its support for distributed computing. It includes APIs for building distributed applications, such as remote method invocation (RMI) and message-driven beans. This allows developers to create applications that can run on multiple servers and communicate with each other seamlessly.

    Another important aspect of Jakarta EE is its focus on security. It provides a comprehensive set of security APIs and features, including authentication, authorization, and encryption. This ensures that applications built with Jakarta EE are robust and protected against potential security threats.

    Portability is also a major advantage of Jakarta EE. It allows developers to write applications that can run on any Jakarta EE-compliant server, regardless of the underlying operating system or hardware. This makes it easier to deploy and maintain applications across different environments.

    In addition, Jakarta EE offers a wide range of APIs and specifications for various enterprise-level services, such as database access, messaging, and web services. This simplifies the development process and allows developers to focus on building business logic rather than dealing with low-level details.

    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
    Information Not Available
    Top Industries
    REVIEWERS
    Computer Software Company30%
    Financial Services Firm15%
    University9%
    Marketing Services Firm6%
    VISITORS READING REVIEWS
    Financial Services Firm25%
    Computer Software Company13%
    Manufacturing Company7%
    Comms Service Provider6%
    VISITORS READING REVIEWS
    Computer Software Company14%
    Financial Services Firm14%
    Comms Service Provider11%
    Government8%
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business24%
    Midsize Enterprise15%
    Large Enterprise61%
    Buyer's Guide
    Apache Spark vs. Jakarta EE
    March 2024
    Find out what your peers are saying about Apache Spark vs. Jakarta EE and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    Apache Spark is ranked 2nd in Java Frameworks with 58 reviews while Jakarta EE is ranked 4th in Java Frameworks with 3 reviews. Apache Spark is rated 8.4, while Jakarta EE is rated 7.4. 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 Jakarta EE writes "A robust enterprise Java capabilities with complex configuration involved, making it a powerful choice for scalable applications while requiring a learning curve". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Amazon Corretto, whereas Jakarta EE is most compared with Spring Boot, Spring MVC, Amazon Corretto, Eclipse MicroProfile and Vert.x. See our Apache Spark vs. Jakarta EE report.

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