Apache Spark vs Eclipse MicroProfile comparison

Apache and Eclipse Foundation are both solutions in the Java Frameworks category. Apache is ranked #2 with an average rating of 8.8, while Eclipse Foundation is ranked #5. Apache holds a 8.4% mindshare in JF, compared to Eclipse Foundation’s 6.9% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 100% of Eclipse Foundation users who would recommend it.

Apache Spark

Read 67 Apache Spark reviews

3,754 Views
1,351 Comparison Views

90% willing to recommend

Eclipse MicroProfile

Read 3 Eclipse MicroProfile reviews

1,146 Views
1,100 Comparison Views

100% willing to recommend

Apache Spark

Eclipse MicroProfile

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache Spark and Eclipse MicroProfile 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. Eclipse MicroProfile Report (Updated: September 2025).

Buyer's Guide

Apache Spark vs. Eclipse MicroProfile

September 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

6.9

Number of Reviews

Ranking in other categories

Hadoop (2nd), Compute Service (4th)

Eclipse MicroProfile

Ranking in Java Frameworks

5th

Average Rating

8.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Java Frameworks category, the mindshare of Apache Spark is 8.4%, up from 7.8% compared to the previous year. The mindshare of Eclipse MicroProfile is 6.9%, down from 7.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks Market Share Distribution
Product	Market Share (%)
Apache Spark	8.4%
Eclipse MicroProfile	6.9%
Other	84.7%

Java Frameworks

Featured Reviews

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Empowering data consolidation and fast decision-making with efficient big data processing

I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.

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Idris Oyibo Igagwu

Integration Developer at FHI 360

Scalable solution with an easy initial setup process

We use the solution for managing large programs, customer interactions, testing, and calculation purposes of our finance-based company The solution's most valuable feature is its ability to support dynamic developer profiles. We can easily create multiple accounts and rooms for different…

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

"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."

"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."

"Provides a lot of good documentation compared to other solutions."

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

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"Spark can handle small to huge data and is suitable for any size of company."

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

"There's a lot of functionality."

More Apache Spark pros

"We use the solution to create microservices."

"The solution is stable."

"Provides a lightweight runtime."

Cons

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

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

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

"The initial setup was not easy."

"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

More Apache Spark cons

"Deployment of microservers in the Kubernetes environment is difficult."

"The tool needs to improve its messaging."

"Its performance speed could be improved while working on the browser."

Pricing and Cost Advice

"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."

"Apache Spark is an open-source tool."

"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."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"Apache Spark is an expensive solution."

"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."

"The product is expensive, considering the setup."

More Apache Spark pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

11%

Manufacturing Company

Comms Service Provider

Financial Services Firm

22%

Manufacturing Company

University

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	27
Midsize Enterprise	15
Large Enterprise	32

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?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

Regarding Apache Spark, I have only used Apache Spark Structured Streaming, not the machine learning components. I am uncertain about specific improvements needed today. However, after five years, ...

See all answers

Which is better - Spring Boot or Eclipse MicroProfile?

Springboot is a Java-based solution that is very popular and easy to use. You can use it to build applications quickly and confidently. Springboot has a very large, helpful learning community, whic...

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Comparisons

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Compared 24% of the time

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SAP HANA vs Apache Spark

Compared 9% of the time

AWS Lambda vs Apache Spark

Compared 8% of the time

Apache NiFi vs Apache Spark

Compared 6% of the time

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Spring Boot vs Eclipse MicroProfile

Compared 84% of the time

Vert.x vs Eclipse MicroProfile

Compared 9% of the time

IBM Open Liberty vs Eclipse MicroProfile

Compared 7% of the time

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Product Reports

Buyer's Guide

Apache Spark

October 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

September 2025

Download Eclipse MicroProfile 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

Many innovative "microservice" Enterprise Java environments and frameworks already exist in the Java ecosystem. These projects are creating new features and capabilities to address microservice architectures -- leveraging both Java EE and non-Java EE technologies.

The goal of the Eclipse MicroProfile project is to iterate and innovate in short cycles to propose new common APIs and functionality, get community approval, release, and repeat. Eventually, the outputs of this project could be submitted to the Eclipse Jakarta EE, JCP, OpenJDK or any relevant standards body.

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. Eclipse MicroProfile

September 2025

Free Report: Apache Spark vs. Eclipse MicroProfile

Find out what your peers are saying about Apache Spark vs. Eclipse MicroProfile and other solutions. Updated: September 2025.

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