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Apache Spark vs Jakarta EE comparison

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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 November 2026, in the Java Frameworks category, the mindshare of Apache Spark is 11.7%, up from 8.5% compared to the previous year. The mindshare of Jakarta EE is 14.3%, down from 15.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Java Frameworks Mindshare Distribution
ProductMindshare (%)
Apache Spark11.7%
Jakarta EE14.3%
Other74.0%
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

"The processing time is very much improved over the data warehouse solution that we were using."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"Apache Spark, specifically PySpark and the tools available there, have been quite helpful in my event analysis work."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"As it uses in-memory data processing, Spark is very fast."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"Aggregations are very fast in our project since we started to use Spark."
"It is a very fast solution."
"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."
"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

"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."
"Apache Spark should add some resource management improvements to the algorithms."
"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."
"The solution needs to optimize shuffling between workers."
"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."
"Dynamic DataFrame options are not yet available."
"The logging for the observability platform could be better."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"It would be great if we could have a UI-based approach or easily include the specific dependencies we need."
"All the customization and all the plugins we can install 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."
 

Pricing and Cost Advice

"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 open-source. You have to pay only when you use any bundled product, such as Cloudera."
"The solution is affordable and there are no additional licensing costs."
"We are using the free version of the solution."
"The product is expensive, considering the setup."
"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."
"It is an open-source solution, it is free of charge."
"I would rate Jakarta EE's pricing seven out of ten."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Outsourcing Company
10%
Comms Service Provider
9%
Construction Company
8%
Financial Services Firm
17%
Manufacturing Company
12%
Comms Service Provider
10%
University
8%
 

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: October 2026.
920,769 professionals have used our research since 2012.