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Apache Spark vs Eclipse MicroProfile comparison

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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
6th
Average Rating
8.4
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of September 2026, in the Java Frameworks category, the mindshare of Apache Spark is 11.9%, up from 8.3% compared to the previous year. The mindshare of Eclipse MicroProfile is 7.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Java Frameworks Mindshare Distribution
ProductMindshare (%)
Apache Spark11.9%
Eclipse MicroProfile7.4%
Other80.7%
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

"I like that Apache Spark can handle multiple tasks parallelly, and I also like the automation feature, while JavaScript helps with the parallel streaming of the library."
"The solution has been very stable."
"Spark Streaming, Spark SQL and MLib in that order."
"The product's initial setup phase was easy."
"Apache Spark, specifically PySpark and the tools available there, have been quite helpful in my event analysis work."
"Spark is relatively easy to deploy, with rich features in handling big data."
"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."
"ETL and streaming capabilities."
"A great feature is the lightweight runtime."
"Provides a lightweight runtime."
"We use the solution to create microservices."
"The solution is stable."
 

Cons

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."
"One limitation is that not all machine learning libraries and models support it."
"I ran into Spark application performance issues."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."
"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"The tool needs to improve its messaging."
"Its performance speed could be improved while working on the browser."
"The most difficult part is deployment of the microservers in the Kubernetes environment and building the mechanisms for automated testing."
 

Pricing and Cost Advice

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"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 not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
"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."
"Spark is an open-source solution, so there are no licensing costs."
"Apache Spark is an expensive solution."
"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."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Outsourcing Company
9%
Comms Service Provider
9%
Construction Company
9%
Financial Services Firm
22%
University
12%
Insurance Company
12%
Comms Service Provider
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
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Find out what your peers are saying about Apache Spark vs. Eclipse MicroProfile and other solutions. Updated: August 2026.
913,683 professionals have used our research since 2012.