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Apache Spark vs Eclipse MicroProfile comparison

 

Comparison Buyer's Guide

Executive Summary

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
69
Ranking in other categories
Hadoop (1st), Compute Service (6th)
Eclipse MicroProfile
Ranking in Java Frameworks
7th
Average Rating
8.4
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of August 2026, in the Java Frameworks category, the mindshare of Apache Spark is 11.8%, up from 8.1% compared to the previous year. The mindshare of Eclipse MicroProfile is 7.4%, up from 7.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Java Frameworks Mindshare Distribution
ProductMindshare (%)
Apache Spark11.8%
Eclipse MicroProfile7.4%
Other80.8%
Java Frameworks
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
FABIO LUIS VELLOSO DA SILVA - PeerSpot reviewer
Partner at SouJava
A scalable and easy to setup solution
We use the solution to create microservices.  The tool needs to improve its messaging.  I don't have much more data or experience with stability.  The solution is scalable.  The tool's setup is easy.  I would rate the product a nine out of ten. It is very important to know Java, and issues…

Quotes from Members

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

Pros

"It is useful for handling large amounts of data, and it is very useful for scientific purposes."
"The main feature that we find valuable is that it is very fast."
"The data processing framework is good."
"Spark is used for transformations from large volumes of data, and it is usefully distributed."
"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."
"The scalability has been the most valuable aspect of the solution."
"This solution provides a clear and convenient syntax for our analytical tasks."
"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."
"Provides a lightweight runtime."
"The solution is stable."
"We use the solution to create microservices."
"A great feature is the lightweight runtime."
 

Cons

"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."
"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 initial set-up is quite complex because you have to set-up many different configuration parameters that are deployment-specific."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"Like I said scalability is still an issue, also stability."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"Better integration of BI tools would be a much appreciated improvement."
"The tool needs to improve its messaging."
"The most difficult part is deployment of the microservers in the Kubernetes environment and building the mechanisms for automated testing."
"Its performance speed could be improved while working on the browser."
 

Pricing and Cost Advice

"The solution is affordable and there are no additional licensing costs."
"Spark is an open-source solution, so there are no licensing costs."
"It is an open-source platform. We do not pay for its subscription."
"Apache Spark is an open-source tool."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"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."
"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Construction Company
9%
Manufacturing Company
8%
Comms Service Provider
7%
Financial Services Firm
19%
University
13%
Insurance Company
10%
Manufacturing Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise33
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...
What is your primary use case for Apache Spark?
I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...
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...
 

Overview

 

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
Find out what your peers are saying about Apache Spark vs. Eclipse MicroProfile and other solutions. Updated: June 2026.
908,800 professionals have used our research since 2012.