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 #6 with an average rating of 8.5. Apache holds a 5.5% mindshare in JF, compared to Eclipse Foundation’s 6.4% 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 65 Apache Spark reviews

2,198 Views
1,690 Comparison Views

90% willing to recommend

Eclipse MicroProfile

Read 3 Eclipse MicroProfile reviews

2,076 Views
1,770 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: March 2025).

Buyer's Guide

Apache Spark vs. Eclipse MicroProfile

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)

Eclipse MicroProfile

Ranking in Java Frameworks

6th

Average Rating

8.4

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 Eclipse MicroProfile is 6.4%, down from 8.0% 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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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

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."

"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."

"Apache Spark provides a very high-quality implementation of distributed data processing."

"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."

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

"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."

"Apache Spark can do large volume interactive data analysis."

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

More Apache Spark pros

"We use the solution to create microservices."

"Provides a lightweight runtime."

"The solution is stable."

Cons

"It should support more programming languages."

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

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"Apache Spark provides very good performance The tuning phase is still tricky."

"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 requires overcoming a significant learning curve due to its robust and feature-rich nature."

"We are building our own queries on Spark, and it can be improved in terms of query handling."

"Dynamic DataFrame options are not yet available."

More Apache Spark cons

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

"The tool needs to improve its messaging."

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

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."

"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."

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

"They provide an open-source license for the on-premise version."

"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."

"The product is expensive, considering the setup."

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

"Apache Spark is an expensive solution."

More Apache Spark pricing and cost advice

Information not available

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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

20%

Computer Software Company

13%

Government

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 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 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

Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

Spring Boot vs Eclipse MicroProfile

Compared 84% of the time

Jakarta EE vs Eclipse MicroProfile

Compared 6% of the time

Open Liberty vs Eclipse MicroProfile

Compared 5% of the time

Vert.x vs Eclipse MicroProfile

Compared 5% of the time

More Eclipse MicroProfile Competitors

Product Reports

Buyer's Guide

Apache Spark

March 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

March 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

March 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: March 2025.

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