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 6.5. Apache holds a 5.7% mindshare in JF, compared to Eclipse Foundation’s 16.9% 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

2,370 Views
1,830 Comparison Views

90% willing to recommend

Jakarta EE

Read 3 Jakarta EE reviews

6,625 Views
5,433 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: January 2025).

Buyer's Guide

Apache Spark vs. Jakarta EE

January 2025

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Helped 839,422 peers since 2012

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 March 2025, in the Java Frameworks category, the mindshare of Apache Spark is 5.7%, down from 7.8% compared to the previous year. The mindshare of Jakarta EE is 16.9%, down from 22.5% 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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Mortaza Ghahramani

Senior Java Software Engineer at eDreams ODIGEO

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.…

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Quotes from Members

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

Pros

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

"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."

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."

"The deployment of the product is easy."

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

"The most valuable feature of this solution is its capacity for processing large amounts of data."

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

"It should support more programming languages."

"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."

"The solution needs to optimize shuffling between workers."

"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."

"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 you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

More Apache Spark cons

"All the customization and plugins can 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."

"Jakarta EE's configuration could be simpler, which would make it more useful as a developer experience."

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."

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

"Apache Spark is an open-source tool."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"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."

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

"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."

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

27%

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...

See all answers

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 9% 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 13% 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

Vert.x 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

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Buyer's Guide

Apache Spark vs. Jakarta EE

January 2025

Free Report: Apache Spark vs. Jakarta EE

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