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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

"We have 1000x improvement in performance over other techniques."
"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."
"Powerful language."
"The main feature that we find valuable is that it is very fast."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"The product is useful for analytics."
"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."
"Configuring, monitoring, and ensuring observability is a straightforward process."
"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."
"Jakarta EE's best features include REST services, configuration, and persistent facilities. It's also incredibly cloud friendly."
 

Cons

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"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."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"Dynamic DataFrame options are not yet available."
"It's not easy to install."
"Like I said scalability is still an issue, also stability."
"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."
"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."
"It would be great if we could have a UI-based approach or easily include the specific dependencies we need."
 

Pricing and Cost Advice

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"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."
"The product is expensive, considering the setup."
"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."
"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 an open-source solution, it is free of charge."
"The solution is affordable and there are no additional licensing costs."
"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.