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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 June 2026, in the Java Frameworks category, the mindshare of Apache Spark is 11.3%, up from 7.9% compared to the previous year. The mindshare of Jakarta EE is 16.9%, up from 16.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Java Frameworks Mindshare Distribution
ProductMindshare (%)
Apache Spark11.3%
Jakarta EE16.9%
Other71.8%
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

"AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."
"The best features in Apache Spark that I appreciate are the fast database access, the data transformation, and the data exchange."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"It allows the loading and investigation of very lard data sets, has MLlib for machine learning, Spark streaming, and both the new and old dataframe API."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"I found the solution stable. We haven't had any problems with it."
"We use Spark to process data from different data sources."
"It is a better MR, supports streaming and micro-batch, and supports Spark ML and Spark SQL."
"The feature that allows a variation of work space based on the application being used."
"Eclipse is now on a good track and they have a very good interface."
"Jakarta EE's best features include REST services, configuration, and persistent facilities. It's also incredibly cloud friendly."
"Configuring, monitoring, and ensuring observability is a straightforward process."
 

Cons

"The migration of data between different versions could be improved."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"There were some problems related to the product's compatibility with a few Python libraries."
"Apache Spark should add some resource management improvements to the algorithms."
"I ran into Spark application performance issues."
"It needs to be simpler to use the machine learning algorithms supported by Octave (example polynomial regressions, polynomial interpolation)."
"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."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"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."
"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 an open-source solution, it is free of charge."
"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."
"We are using the free version of the solution."
"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 quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"Apache Spark is an expensive solution."
"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 an open-source platform. We do not pay for its subscription."
"I would rate Jakarta EE's pricing seven out of ten."
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Manufacturing Company
9%
Construction Company
8%
Comms Service Provider
7%
Financial Services Firm
15%
Comms Service Provider
11%
Manufacturing Company
11%
University
9%
 

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.
900,644 professionals have used our research since 2012.