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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

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"It is an excellent tool to process massive amount of data."
"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."
"It provides a scalable machine learning library."
"The main feature that we find valuable is that it is very fast."
"The scalability has been the most valuable aspect of the solution."
"Aggregations are very fast in our project since we started to use Spark."
"This solution provides a clear and convenient syntax for our analytical tasks."
"Eclipse is now on a good track and they have a very good interface."
"Configuring, monitoring, and ensuring observability is a straightforward process."
"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."
 

Cons

"The initial set-up is quite complex because you have to set-up many different configuration parameters that are deployment-specific."
"There is complexity when it comes to understanding the whole ecosystem, especially for beginners."
"They currently use a JDK version which is a little bit old. Not all features are on it."
"The product could improve the user interface and make it easier for new users."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"The initial setup was complex. It was not easy getting the correct version and dependencies set up."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."
"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."
"All the customization and all the plugins we can install make the interface too slow and heavy in some situations."
 

Pricing and Cost Advice

"The product is expensive, considering the setup."
"We are using the free version of the solution."
"It is an open-source solution, it is free of charge."
"Spark is an open-source solution, so there are no licensing costs."
"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."
"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."
"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."
"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
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Find out what your peers are saying about Apache Spark vs. Jakarta EE and other solutions. Updated: June 2026.
908,800 professionals have used our research since 2012.