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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
7.7
Number of Reviews
65
Ranking in other categories
Hadoop (1st), Compute Service (4th)
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 February 2025, in the Java Frameworks category, the mindshare of Apache Spark is 5.9%, down from 7.9% compared to the previous year. The mindshare of Jakarta EE is 18.1%, down from 22.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Java Frameworks
 

Featured Reviews

Ilya Afanasyev - PeerSpot reviewer
Reliable, able to expand, and handle large amounts of data well
We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.
Mortaza Ghahramani - PeerSpot reviewer
Cloud-friendly but has problems with configuration
I primarily use Jakarta EE as a business enterprise product Jakarta EE's best features include REST services, configuration, and persistent facilities. It's also incredibly cloud friendly. Jakarta EE's configuration could be simpler, which would make it more useful as a developer experience.…

Quotes from Members

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

Pros

"I feel the streaming is its best feature."
"Spark can handle small to huge data and is suitable for any size of company."
"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"There's a lot of functionality."
"The data processing framework is good."
"The most valuable feature of Apache Spark is its flexibility."
"It provides a scalable machine learning library."
"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."
"The feature that allows a variation of work space based on the application being used."
 

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."
"Apache Spark should add some resource management improvements to the algorithms."
"The solution’s integration with other platforms should be improved."
"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."
"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."
"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."
"The Spark solution could improve in scheduling tasks and managing dependencies."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"All the customization and plugins can 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."
"It would be great if we could have a UI-based approach or easily include the specific dependencies we need."
 

Pricing and Cost Advice

"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."
"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"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."
"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"I would rate Jakarta EE's pricing seven out of ten."
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
7%
Retailer
5%
Financial Services Firm
15%
Computer Software Company
14%
Comms Service Provider
7%
Government
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
What is your experience regarding pricing and costs for Apache Spark?
Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...
What needs improvement with Apache Spark?
The main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...
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...
What do you like most about Jakarta EE?
Configuring, monitoring, and ensuring observability is a straightforward process.
What needs improvement with Jakarta EE?
Enhancements in configurations can be achieved by benchmarking against Spring Boot technology. It would be great if we could have a UI-based approach or easily include the specific dependencies we ...
 

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: January 2025.
832,138 professionals have used our research since 2012.