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 8.8, while Eclipse Foundation is ranked #3 with an average rating of 7.0. Apache holds a 8.3% mindshare in JF, compared to Eclipse Foundation’s 15.1% 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 67 Apache Spark reviews

4,144 Views
1,492 Comparison Views

90% willing to recommend

Jakarta EE

Read 3 Jakarta EE reviews

4,104 Views
4,104 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: July 2025).

Buyer's Guide

Apache Spark vs. Jakarta EE

July 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

7.3

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (3rd)

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 September 2025, in the Java Frameworks category, the mindshare of Apache Spark is 8.3%, up from 7.9% compared to the previous year. The mindshare of Jakarta EE is 15.1%, down from 27.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks Market Share Distribution
Product	Market Share (%)
Apache Spark	8.3%
Jakarta EE	15.1%
Other	76.6%

Java Frameworks

Featured Reviews

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Empowering data consolidation and fast decision-making with efficient big data processing

I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.

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Erick Karanja

Technical Lead at Cellulant Kenya

A robust enterprise Java capabilities with complex configuration involved, making it a powerful choice for scalable applications while requiring a learning curve

When running applications in the cloud, scalability is highly dependent on how you configure it. Factors such as the number of instances you want to scale, and the threshold for scaling based on the quantity of messages or the amount of data, are all customizable based on your application's needs.

Read full review

Quotes from Members

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

Pros

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"Spark can handle small to huge data and is suitable for any size of company."

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"I found the solution stable. We haven't had any problems with it."

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

"Apache Spark provides a very high-quality implementation of distributed data processing."

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

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

"Apache Spark provides very good performance The tuning phase is still tricky."

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"Apache Spark lacks geospatial data."

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"The solution needs to optimize shuffling between workers."

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

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

More Apache Spark cons

"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."

"Jakarta EE's configuration could be simpler, which would make it more useful as a developer experience."

Pricing and Cost Advice

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

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"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."

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

"We are using the free version of the solution."

"The solution is affordable and there are no additional licensing costs."

"They provide an open-source license for the on-premise version."

"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."

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

26%

Computer Software Company

11%

Manufacturing Company

Comms Service Provider

Financial Services Firm

15%

Computer Software Company

14%

Comms Service Provider

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	27
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?

Regarding Apache Spark, I have only used Apache Spark Structured Streaming, not the machine learning components. I am uncertain about specific improvements needed today. However, after five years, ...

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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...

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What do you like most about Jakarta EE?

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

See all answers

What needs improvement with Jakarta EE?

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 ...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 24% of the time

AWS Batch vs Apache Spark

Compared 10% of the time

SAP HANA vs Apache Spark

Compared 10% of the time

AWS Lambda vs Apache Spark

Compared 7% of the time

Azure Stream Analytics vs Apache Spark

Compared 2% of the time

More Apache Spark Competitors

Spring Boot vs Jakarta EE

Compared 62% of the time

Amazon Corretto vs Jakarta EE

Compared 10% of the time

Spring MVC vs Jakarta EE

Compared 10% of the time

Oracle Application Development Framework vs Jakarta EE

Compared 6% of the time

Eclipse MicroProfile vs Jakarta EE

Compared 3% of the time

More Jakarta EE Competitors

Product Reports

Buyer's Guide

Apache Spark

September 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

August 2025

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

July 2025

Free Report: Apache Spark vs. Jakarta EE

Find out what your peers are saying about Apache Spark vs. Jakarta EE and other solutions. Updated: July 2025.

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