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Apache Spark vs Jakarta EE comparison

 

Comparison Buyer's Guide

Executive Summary

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
69
Ranking in other categories
Hadoop (1st), Compute Service (6th)
Jakarta EE
Ranking in Java Frameworks
3rd
Average Rating
7.4
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of August 2026, in the Java Frameworks category, the mindshare of Apache Spark is 11.8%, up from 8.1% compared to the previous year. The mindshare of Jakarta EE is 16.6%, up from 15.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Java Frameworks Mindshare Distribution
ProductMindshare (%)
Apache Spark11.8%
Jakarta EE16.6%
Other71.6%
Java Frameworks
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
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.
Erick  Karanja - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms."
"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The product's initial setup phase was easy."
"With Spark SQL we've now the capabilities to analyse very large quantities of data located in S3 on Amazon at very low cost comparing other solution we checked."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"The scalability has been the most valuable aspect of the solution."
"It is an excellent tool to process massive amount of data."
"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."
"Eclipse is now on a good track and they have a very good interface."
"Configuring, monitoring, and ensuring observability is a straightforward process."
 

Cons

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."
"There is complexity when it comes to understanding the whole ecosystem, especially for beginners."
"Apache Spark provides very good performance The tuning phase is still tricky."
"The basic improvement would be to have integration with these solutions."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"Better data lineage support."
"Like I said scalability is still an issue, also stability."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"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 all the plugins we can install make the interface too slow and heavy in some situations."
 

Pricing and Cost Advice

"It is an open-source solution, it is free of charge."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"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."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
"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."
"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 an open-source solution, and there is no cost involved in deploying the solution on-premises."
"I would rate Jakarta EE's pricing seven out of ten."
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Construction Company
9%
Manufacturing Company
8%
Comms Service Provider
7%
Financial Services Firm
16%
Comms Service Provider
10%
Manufacturing Company
10%
University
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise33
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...
What is your primary use case for Apache Spark?
I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...
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...
 

Comparisons

 

Overview

 

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
Find out what your peers are saying about Apache Spark vs. Jakarta EE and other solutions. Updated: June 2026.
908,834 professionals have used our research since 2012.