Apache Spark vs Jakarta EE comparison

Apache and Eclipse Foundation are both solutions in the Java Frameworks category. Apache is ranked #2 with an average rating of 8.7, while Eclipse Foundation is ranked #3. Apache holds a 9.0% mindshare in JF, compared to Eclipse Foundation’s 16.8% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 66% of Eclipse Foundation users who would recommend it.

Apache Spark

Read 68 Apache Spark reviews

3,530 Views
1,165 Comparison Views

90% willing to recommend

Jakarta EE

Read 3 Jakarta EE reviews

2,805 Views
2,805 Comparison Views

66% willing to recommend

Apache Spark

Jakarta EE

Comparison Buyer's Guide

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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: December 2025).

Buyer's Guide

Apache Spark vs. Jakarta EE

December 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Apache Spark

Ranking in Java Frameworks

2nd

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (5th)

Jakarta EE

Ranking in Java Frameworks

3rd

Average Rating

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Java Frameworks category, the mindshare of Apache Spark is 9.0%, up from 7.7% compared to the previous year. The mindshare of Jakarta EE is 16.8%, down from 23.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks Market Share Distribution
Product	Market Share (%)
Apache Spark	9.0%
Jakarta EE	16.8%
Other	74.2%

Java Frameworks

Featured Reviews

Devindra Weerasooriya

Data Architect at Devtech

Provides a consistent framework for building data integration and access solutions with reliable performance

The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

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Mortaza Ghahramani

Senior Java Software Engineer at eDreams ODIGEO

Cloud-friendly but has problems with configuration

I primarily use Jakarta EE as a business enterprise product Jakarta EE's best features include REST services, configuration, and persistent facilities. It's also incredibly cloud friendly. Jakarta EE's configuration could be simpler, which would make it more useful as a developer experience.…

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"ETL and streaming capabilities."

"The data processing framework is good."

"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."

"I feel the streaming is its best feature."

"The scalability has been the most valuable aspect of the solution."

"The solution has been very stable."

"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."

More Apache Spark pros

"Jakarta EE's best features include REST services, configuration, and persistent facilities. It's also incredibly cloud friendly."

"The feature that allows a variation of work space based on the application being used."

"Configuring, monitoring, and ensuring observability is a straightforward process."

Cons

"The product could improve the user interface and make it easier for new users."

"There were some problems related to the product's compatibility with a few Python libraries."

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"The basic improvement would be to have integration with these solutions."

"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."

"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."

More Apache Spark cons

"It would be great if we could have a UI-based approach or easily include the specific dependencies we need."

"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."

Pricing and Cost Advice

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"Spark is an open-source solution, so there are no licensing costs."

"It is an open-source platform. We do not pay for its subscription."

"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."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

More Apache Spark pricing and cost advice

"I would rate Jakarta EE's pricing seven out of ten."

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

Manufacturing Company

Comms Service Provider

Financial Services Firm

14%

Computer Software Company

12%

Comms Service Provider

10%

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	15
Large Enterprise	32

No data available

Questions from the Community

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...

See all answers

Which is better - Spring Boot or Jakarta EE?

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 offe...

See all answers

Comparisons

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Compared 16% of the time

AWS Lambda vs Apache Spark

Compared 7% of the time

AWS Batch vs Apache Spark

Compared 6% of the time

Apache NiFi vs Apache Spark

Compared 5% of the time

Spot by Flexera vs Apache Spark

Compared 2% of the time

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Spring Boot vs Jakarta EE

Compared 69% of the time

Amazon Corretto vs Jakarta EE

Compared 7% of the time

Spring MVC vs Jakarta EE

Compared 7% of the time

Oracle Application Development Framework vs Jakarta EE

Compared 5% of the time

Vert.x vs Jakarta EE

Compared 5% of the time

More Jakarta EE Competitors

Product Reports

Buyer's Guide

Apache Spark

January 2026

Download Apache Spark product report

Buyer's Guide

Java Frameworks

January 2026

Download Jakarta EE product report

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

Apache

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.

Eclipse Foundation

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

Buyer's Guide

Apache Spark vs. Jakarta EE

December 2025

Free Report: Apache Spark vs. Jakarta EE

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