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 8.8, while GitHub is ranked #10. Apache holds a 8.3% mindshare in JF, compared to GitHub’s 0.3% 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 67 Apache Spark reviews

4,144 Views
1,492 Comparison Views

90% willing to recommend

npm

Read 5 npm reviews

164 Views
164 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: July 2025).

Buyer's Guide

Apache Spark vs. npm

July 2025

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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

7.3

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (3rd)

npm

Ranking in Java Frameworks

10th

Average Rating

8.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2025, in the Java Frameworks category, the mindshare of Apache Spark is 8.3%, up from 7.9% compared to the previous year. The mindshare of npm is 0.3%, 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	8.3%
npm	0.3%
Other	91.4%

Java Frameworks

Featured Reviews

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Empowering data consolidation and fast decision-making with efficient big data processing

I can improve the organization's functions by taking less time to make decisions. To make the right decision, you need the right data, and a solution can provide this by hiring talent and employees who can consolidate data from different sources and organize it. Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming. To make the right decision, you should have both accurate and fast data. Apache Spark itself is similar to the Python programming language. Python is a language with many libraries for mathematics and machine learning. Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code. Within it, there are many APIs, including SQL APIs, allowing you to write SQL code within a Python function in Apache Spark. You can also use Apache Spark Structured Streaming and machine learning APIs.

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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

"The most valuable feature of Apache Spark is its flexibility."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

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

"The fault tolerant feature is provided."

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

"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."

"Spark can handle small to huge data and is suitable for any size of company."

"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."

More Apache Spark pros

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

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

"The solution is scalable."

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

Cons

"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."

"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."

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

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

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

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

"In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that."

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

More Apache Spark cons

"The library could be updated."

"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."

"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."

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

Pricing and Cost Advice

"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."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

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

"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."

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

"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."

"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."

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

"NPM is an open-source solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

11%

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	27
Midsize Enterprise	15
Large Enterprise	32

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.

See all answers

What needs improvement with Apache Spark?

Regarding Apache Spark, I have only used Apache Spark Structured Streaming, not the machine learning components. I am uncertain about specific improvements needed today. However, after five years, ...

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Comparisons

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AWS Lambda vs Apache Spark

Compared 7% of the time

Spark SQL vs Apache Spark

Compared 6% of the time

More Apache Spark Competitors

No data available

Product Reports

Buyer's Guide

Apache Spark

September 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

August 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

July 2025

Free Report: Apache Spark vs. npm

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

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