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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

"Spark can handle small to huge data and is suitable for any size of company."
"I feel the streaming is its best feature."
"The best features in Apache Spark that I appreciate are the fast database access, the data transformation, and the data exchange."
"ETL and streaming capabilities."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"The most valuable feature of this solution is its capacity for processing large amounts of data."
"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."
"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."
"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."
"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."
 

Cons

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."
"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."
"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 integration of BI tools would be a much appreciated improvement."
"I have to say it is bad. I can only ask for help in the Google group."
"The main concern is the overhead of Java when distributed processing is not necessary."
"Apache Spark's GUI and scalability could be improved."
"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."
"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

"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."
"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 an open-source solution, and there is no cost involved in deploying the solution on-premises."
"The solution is affordable and there are no additional licensing costs."
"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 solution, it is free of charge."
"Spark is an open-source solution, so there are no licensing costs."
"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."
"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
9%
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.