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 8.6, while Eclipse Foundation is ranked #4 with an average rating of 8.5. Apache holds a 7.9% mindshare in JF, compared to Eclipse Foundation’s 7.3% 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 66 Apache Spark reviews

1,741 Views
1,288 Comparison Views

90% willing to recommend

Eclipse MicroProfile

Read 3 Eclipse MicroProfile reviews

1,453 Views
1,186 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: June 2025).

Buyer's Guide

Apache Spark vs. Eclipse MicroProfile

June 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

7.4

Number of Reviews

Ranking in other categories

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

Eclipse MicroProfile

Ranking in Java Frameworks

4th

Average Rating

8.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Java Frameworks category, the mindshare of Apache Spark is 7.9%, down from 8.3% compared to the previous year. The mindshare of Eclipse MicroProfile is 7.3%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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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…

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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 features we find most valuable are the machine learning, data learning, and Spark Analytics."

"The solution has been very stable."

"The fault tolerant feature is provided."

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"There's a lot of functionality."

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

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

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."

More Apache Spark pros

"Provides a lightweight runtime."

"We use the solution to create microservices."

"The solution is stable."

Cons

"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."

"The logging for the observability platform could be better."

"Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing."

"It's not easy to install."

"Apache Spark's GUI and scalability could be improved."

"The solution must improve its performance."

"Apache Spark should add some resource management improvements to the algorithms."

"One limitation is that not all machine learning libraries and models support it."

More Apache Spark cons

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

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

"The tool needs to improve its messaging."

Pricing and Cost Advice

"The solution is affordable and there are no additional licensing costs."

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

"The product is expensive, considering the setup."

"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."

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

"It is an open-source solution, it is free of charge."

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

More Apache Spark pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

12%

Manufacturing Company

Comms Service Provider

Financial Services Firm

24%

Computer Software Company

Manufacturing Company

Government

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?

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

See all answers

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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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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What do you like most about Eclipse MicroProfile?

The solution is stable.

See all answers

What needs improvement with Eclipse MicroProfile?

The solution's performance speed could be better while working on the browser. Also, they should include an option for online publishing. It will make sharing work easier. We can just publish work ...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 25% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 10% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

More Apache Spark Competitors

Spring Boot vs Eclipse MicroProfile

Compared 82% of the time

IBM Open Liberty vs Eclipse MicroProfile

Compared 7% of the time

Vert.x vs Eclipse MicroProfile

Compared 6% of the time

Jakarta EE vs Eclipse MicroProfile

Compared 5% of the time

More Eclipse MicroProfile Competitors

Product Reports

Buyer's Guide

Apache Spark

June 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

June 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

June 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: June 2025.

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