Apache Spark vs npm comparison

Apache and GitHub are both solutions in the Java Frameworks category. Apache is ranked #2 with an average rating of 8.7, while GitHub is ranked #10. Apache holds a 9.0% mindshare in JF, compared to GitHub’s 0.9% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 100% of GitHub users who would recommend it.

Apache Spark

Read 68 Apache Spark reviews

3,530 Views
1,165 Comparison Views

90% willing to recommend

npm

Read 6 npm reviews

186 Views
180 Comparison Views

100% willing to recommend

Apache Spark

npm

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Executive Summary

We performed a comparison between Apache Spark and npm 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. npm Report (Updated: December 2025).

Buyer's Guide

Apache Spark vs. npm

December 2025

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Helped 881,114 peers since 2012

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

Ranking in other categories

Hadoop (1st), Compute Service (5th)

npm

Ranking in Java Frameworks

10th

Average Rating

9.0

Number of Reviews

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 npm is 0.9%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks Market Share Distribution
Product	Market Share (%)
Apache Spark	9.0%
npm	0.9%
Other	90.1%

Java Frameworks

Featured Reviews

Devindra Weerasooriya

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.

Read full review

Harsh Dabas

Senior QA Automation Engineer at LogMeIn, Inc.

Automation has boosted test coverage and now supports reliable CI workflows

BrowserStack is able to solve every issue which we have been facing, so I would rate it as a 10 out of 10 in terms of my experience. Regarding pricing, I think the organization takes care of that. I am not really aware of the pricing of this, but I have made some personal projects as well in which I use GitHub Actions for free. We can push our code as well. As of now, I am using the free version for my personal use. For the organization, I think the organization is taking care of the same, so we do not need to bother about it.

Read full review

Quotes from Members

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

Pros

"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."

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

"The product's initial setup phase was easy."

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

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"ETL and streaming capabilities."

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

More Apache Spark pros

"The reversal build, gendered build, migrated PCA, and CT features are excellent."

"The most valuable feature of NPM is to trigger APMs."

"It's an open-source setting that's very scalable and easily approachable. I like that you can plug in many features to my product."

"The solution is scalable."

"The product's most valuable feature is dependency installation."

Cons

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

"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."

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

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

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

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

"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."

More Apache Spark cons

"NPM can improve the package manager. For the packages we download for our APM studio to trigger our APM driver, it would benefit if we could have the latest version of NuGet Package Manager within the package manager control. For example, Visual Studio would be good. Then it would be easy for us to get the package manager from there instead of Googling it out and matching it with the current version. It would be less time-consuming for us."

"Some of the libraries that we try to use in npm have issues with security. Also, because it's an open-source solution, I think there are lots of challenges with security. So, the security layer could be improved."

"I would like to see compatible versions, and what new features they will be providing. If it is a useful feature I can merge it. If it is not a usable feature, then I can ignore the newer version."

"The product should be compatible with various programming languages, including both native and upcoming languages."

"The library could be updated."

Pricing and Cost Advice

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

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

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

"Apache Spark is an expensive solution."

"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."

"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."

"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

"It's an open-source solution, and there are no hidden fees."

"We use the open-source version, so it is free."

"The licensing cost is around one hundred and fifty dollars on a quarterly basis."

"NPM is an open-source solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

Computer Software Company

Manufacturing Company

Comms Service Provider

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	15
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	1

Questions from the Community

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

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What is your experience regarding pricing and costs for Apache Spark?

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

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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...

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Comparisons

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Compared 6% of the time

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Compared 5% of the time

SAP HANA vs Apache Spark

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Product Reports

Buyer's Guide

Apache Spark

January 2026

Download Apache Spark product report

Buyer's Guide

Java Frameworks

January 2026

Download npm 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

npm is the package manager for the Node JavaScript platform. It puts modules in place so that node can find them, and manages dependency conflicts intelligently. It is extremely configurable to support a wide variety of use cases. Most commonly, it is used to publish, discover, install, and develop node programs.

GitHub

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

slack, microsoft, netflix, adobe, docker, visa, splunk, zillow

Buyer's Guide

Apache Spark vs. npm

December 2025

Free Report: Apache Spark vs. npm

Find out what your peers are saying about Apache Spark vs. npm and other solutions. Updated: December 2025.

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