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 8.6, while Eclipse Foundation is ranked #3 with an average rating of 7.0. Apache holds a 8.1% mindshare in JF, compared to Eclipse Foundation’s 15.4% 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

4,468 Views
1,608 Comparison Views

90% willing to recommend

Jakarta EE

Read 3 Jakarta EE reviews

4,429 Views
4,429 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 (4th)

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 August 2025, in the Java Frameworks category, the mindshare of Apache Spark is 8.1%, up from 8.0% compared to the previous year. The mindshare of Jakarta EE is 15.4%, down from 27.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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.

Read full review

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

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"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 like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

"Provides a lot of good documentation compared to other solutions."

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

"The fault tolerant feature is provided."

"The most valuable feature of Apache Spark is its ease of use."

"Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms."

More Apache Spark pros

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

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

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

Cons

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

"At the initial stage, the product provides no container logs to check the activity."

"We are building our own queries on Spark, and it can be improved in terms of query handling."

"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."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"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."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

More Apache Spark cons

"All the customization and plugins can make the interface too slow and heavy in some situations."

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

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

Pricing and Cost Advice

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

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

"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."

"The product is expensive, considering the setup."

"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 an open-source solution, and there is no cost involved in deploying the solution on-premises."

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

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."

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

10%

Manufacturing Company

Comms Service Provider

Financial Services Firm

15%

Computer Software Company

14%

Comms Service Provider

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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.

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What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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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 23% of the time

AWS Batch vs Apache Spark

Compared 10% of the time

SAP HANA vs Apache Spark

Compared 9% of the time

Spark SQL 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

Spring MVC vs Jakarta EE

Compared 10% of the time

Amazon Corretto 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 2% of the time

More Jakarta EE Competitors

Product Reports

Buyer's Guide

Apache Spark

August 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

July 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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