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 8.4% 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

3,754 Views
1,351 Comparison Views

90% willing to recommend

Jakarta EE

Read 3 Jakarta EE reviews

3,654 Views
3,654 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: September 2025).

Buyer's Guide

Apache Spark vs. Jakarta EE

September 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 (2nd), 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 October 2025, in the Java Frameworks category, the mindshare of Apache Spark is 8.4%, up from 7.8% compared to the previous year. The mindshare of Jakarta EE is 15.1%, down from 27.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks Market Share Distribution
Product	Market Share (%)
Apache Spark	8.4%
Jakarta EE	15.1%
Other	76.5%

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.

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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."

"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."

"There's a lot of functionality."

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

"The product's initial setup phase was easy."

"The data processing framework is good."

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

"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."

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

"The Spark solution could improve in scheduling tasks and managing dependencies."

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

"They could improve the issues related to programming language for the platform."

"The migration of data between different versions could be improved."

"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."

"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."

"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."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

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

"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."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as 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."

"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."

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

"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 an open-source solution, it is free of charge."

"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."

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 9% of the time

AWS Lambda vs Apache Spark

Compared 7% of the time

Spring MVC vs Apache Spark

Compared 2% of the time

More Apache Spark Competitors

Spring Boot vs Jakarta EE

Compared 64% 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 7% of the time

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Product Reports

Buyer's Guide

Apache Spark

September 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

September 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

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Buyer's Guide

Apache Spark vs. Jakarta EE

September 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: September 2025.

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