Apache Spark vs Jakarta EE comparison

Apache and Eclipse Foundation are both solutions in the Java Frameworks category. Apache is ranked #2 with an average rating of 8.6, while Eclipse Foundation is ranked #3 with an average rating of 7.0. Apache holds a 5.5% mindshare in JF, compared to Eclipse Foundation’s 15.4% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 66% of Eclipse Foundation users who would recommend it.

Apache Spark

Read 65 Apache Spark reviews

2,198 Views
1,690 Comparison Views

90% willing to recommend

Jakarta EE

Read 3 Jakarta EE reviews

6,212 Views
5,066 Comparison Views

66% willing to recommend

Apache Spark

Jakarta EE

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache Spark and Jakarta EE based on real PeerSpot user reviews.

Find out in this report how the two Java Frameworks solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Spark vs. Jakarta EE Report (Updated: March 2025).

Buyer's Guide

Apache Spark vs. Jakarta EE

March 2025

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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

Ranking in other categories

Hadoop (1st), Compute Service (4th)

Jakarta EE

Ranking in Java Frameworks

3rd

Average Rating

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Java Frameworks category, the mindshare of Apache Spark is 5.5%, down from 7.5% compared to the previous year. The mindshare of Jakarta EE is 15.4%, down from 22.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

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.

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Erick Karanja

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.

Read full review

Quotes from Members

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

Pros

"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."

"I found the solution stable. We haven't had any problems with it."

"The solution is very stable."

"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."

"ETL and streaming capabilities."

"The good performance. The nice graphical management console. The long list of ML algorithms."

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"The most valuable feature of Apache Spark is its flexibility."

More Apache Spark pros

"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."

"Configuring, monitoring, and ensuring observability is a straightforward process."

Cons

"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."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"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."

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"The product could improve the user interface and make it easier for new users."

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"It should support more programming languages."

More Apache Spark cons

"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 plugins can make the interface too slow and heavy in some situations."

Pricing and Cost Advice

"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."

"It is an open-source platform. We do not pay for its subscription."

"Apache Spark is an open-source tool."

"We are using the free version of the solution."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"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."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"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."

More Apache Spark pricing and cost advice

"I would rate Jakarta EE's pricing seven out of ten."

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Top Industries

By visitors reading reviews

Financial Services Firm

28%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

15%

Computer Software Company

14%

Comms Service Provider

Manufacturing Company

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.

See all answers

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...

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What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

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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...

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What do you like most about Jakarta EE?

Configuring, monitoring, and ensuring observability is a straightforward process.

See all answers

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 ...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% of the time

Amazon Corretto vs Apache Spark

Compared 2% of the time

More Apache Spark Competitors

Spring Boot vs Jakarta EE

Compared 68% of the time

Spring MVC vs Jakarta EE

Compared 12% of the time

Amazon Corretto vs Jakarta EE

Compared 8% of the time

Oracle Application Development Framework vs Jakarta EE

Compared 3% of the time

Eclipse MicroProfile vs Jakarta EE

Compared 3% of the time

More Jakarta EE Competitors

Product Reports

Buyer's Guide

Apache Spark

March 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

March 2025

Download Jakarta EE product report

Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

Jakarta EE is a powerful platform for developing enterprise-level Java applications. It provides a set of specifications and APIs that enable developers to build scalable, secure, and portable applications. Jakarta EE is built on the foundation of Java EE, which has been widely adopted by organizations around the world.

One of the key features of Jakarta EE is its support for distributed computing. It includes APIs for building distributed applications, such as remote method invocation (RMI) and message-driven beans. This allows developers to create applications that can run on multiple servers and communicate with each other seamlessly.

Another important aspect of Jakarta EE is its focus on security. It provides a comprehensive set of security APIs and features, including authentication, authorization, and encryption. This ensures that applications built with Jakarta EE are robust and protected against potential security threats.

Portability is also a major advantage of Jakarta EE. It allows developers to write applications that can run on any Jakarta EE-compliant server, regardless of the underlying operating system or hardware. This makes it easier to deploy and maintain applications across different environments.

In addition, Jakarta EE offers a wide range of APIs and specifications for various enterprise-level services, such as database access, messaging, and web services. This simplifies the development process and allows developers to focus on building business logic rather than dealing with low-level details.

Eclipse Foundation

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

Buyer's Guide

Apache Spark vs. Jakarta EE

March 2025

Free Report: Apache Spark vs. Jakarta EE

Find out what your peers are saying about Apache Spark vs. Jakarta EE and other solutions. Updated: March 2025.

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