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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
68
Ranking in other categories
Hadoop (1st), Compute Service (5th)
Eclipse MicroProfile
Ranking in Java Frameworks
4th
Average Rating
8.4
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2026, in the Java Frameworks category, the mindshare of Apache Spark is 9.0%, up from 7.7% compared to the previous year. The mindshare of Eclipse MicroProfile is 7.1%, down from 7.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Java Frameworks Market Share Distribution
ProductMarket Share (%)
Apache Spark9.0%
Eclipse MicroProfile7.1%
Other83.9%
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.
Idris Oyibo Igagwu - PeerSpot reviewer
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…

Quotes from Members

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

Pros

"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"The most valuable feature of Apache Spark is its flexibility."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The product is useful for analytics."
"I found the solution stable. We haven't had any problems with it."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."
"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."
"The solution is stable."
"Provides a lightweight runtime."
"We use the solution to create microservices."
 

Cons

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."
"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."
"Very often in many of my experiments, the data set has had to be partitioned, and there have been issues in handling very large data sets, with most of my work done using Python machine learning libraries, requiring chunking, and speed of prediction has been an issue of concern in some experiments where we have had to shut down processes due to CPU requirements, then restart with different Apache configurations, and resourcing support is a major determinant if I were to name a constraint in terms of running machine learning experiments."
"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."
"Apache Spark lacks geospatial data."
"The main concern is the overhead of Java when distributed processing is not necessary."
"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."
"At the initial stage, the product provides no container logs to check the activity."
"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."
"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."
"We are using the free version of the solution."
"It is an open-source solution, it is free of charge."
"The product is expensive, considering the setup."
"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."
"Spark is an open-source solution, so there are no licensing costs."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
9%
Manufacturing Company
7%
Comms Service Provider
6%
Financial Services Firm
20%
University
11%
Manufacturing Company
10%
Insurance Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise15
Large Enterprise32
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.
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?
Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...
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: December 2025.
881,114 professionals have used our research since 2012.