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 8.6, while GitHub is ranked #6 with an average rating of 9.0. Apache holds a 7.9% mindshare in JF, compared to GitHub’s 0.2% 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 66 Apache Spark reviews

4,837 Views
1,741 Comparison Views

90% willing to recommend

npm

Read 5 npm reviews

152 Views
152 Comparison Views

100% willing to recommend

Apache Spark

npm

Comparison Buyer's Guide

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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: June 2025).

Buyer's Guide

Apache Spark vs. npm

June 2025

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Helped 862,077 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

7.4

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (4th)

npm

Ranking in Java Frameworks

6th

Average Rating

8.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Java Frameworks category, the mindshare of Apache Spark is 7.9%, down from 8.3% compared to the previous year. The mindshare of npm is 0.2%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Open-source solution for data processing with portability

Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly. While many choices now exist, Spark remains easy to use, particularly with Python. You can utilize familiar programming styles similar to Pandas in Python, including object-oriented programming. Another advantage is its portability. I can prototype and perform some initial tasks on my laptop using Spark without needing to be on Databricks or any cloud platform. I can transfer it to Databricks or other platforms, such as AWS. This flexibility allows me to improve processing even on my laptop. For instance, if I'm processing large amounts of data and find my laptop becoming slow, I can quickly switch to Spark. It handles small and large datasets efficiently, making it a versatile tool for various data processing needs.

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Puneeth Babu

Senior Technical Consultant at FinXL IT Professional Services

Is scalable, easily approachable, stable, and easy to set up

There are a lot of features that are very fast in npm, even though it was developed 10 or 12 years back. It comes with a bundle or library, so your development time will radically reduce to half. If you need to spin up a new server or you need to have a developer at minimum cost, it can be easily achieved within npm. Overall, I give npm a nine out of ten.

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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

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

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

"I feel the streaming is its best feature."

"The product's deployment phase is easy."

"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."

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

"This solution provides a clear and convenient syntax for our analytical tasks."

"The product is useful for analytics."

More Apache Spark pros

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

"The solution is scalable."

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

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

Cons

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

"It's not easy to install."

"We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data."

"They could improve the issues related to programming language for the platform."

"The product could improve the user interface and make it easier for new users."

"The migration of data between different versions could be improved."

"At the initial stage, the product provides no container logs to check the activity."

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

More Apache Spark cons

"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."

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

"The library could be updated."

"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."

"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."

Pricing and Cost Advice

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

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

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"Spark is an open-source solution, so there are no licensing costs."

"Apache Spark is an expensive solution."

"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."

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

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

More Apache Spark pricing and cost advice

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

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

"NPM is an open-source solution."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

12%

Manufacturing Company

Comms Service Provider

No data available

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?

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

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What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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What do you like most about NPM?

The product's most valuable feature is dependency installation.

See all answers

What needs improvement with NPM?

The product should be compatible with various programming languages, including both native and upcoming languages. There should be an extension for C++ language as many customers prefer it for the ...

See all answers

What is your primary use case for NPM?

We use the product as a packet manager for orchestration and dashboard management. It helps in running the development server.

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 25% of the time

AWS Batch vs Apache Spark

Compared 11% of the time

SAP HANA vs Apache Spark

Compared 10% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

More Apache Spark Competitors

No data available

Product Reports

Buyer's Guide

Apache Spark

July 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

June 2025

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

June 2025

Free Report: Apache Spark vs. npm

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

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