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 #7 with an average rating of 9.0. Apache holds a 5.6% mindshare in JF, compared to GitHub’s 0.1% 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

2,198 Views
1,690 Comparison Views

90% willing to recommend

npm

Read 5 npm reviews

123 Views
21 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: April 2025).

Buyer's Guide

Apache Spark vs. npm

April 2025

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Helped 849,963 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

7.7

Number of Reviews

Ranking in other categories

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

npm

Ranking in Java Frameworks

7th

Average Rating

8.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the Java Frameworks category, the mindshare of Apache Spark is 5.6%, down from 7.4% compared to the previous year. The mindshare of npm is 0.1%, down from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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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

"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."

"The main feature that we find valuable is that it is very fast."

"It provides a scalable machine learning library."

"The most valuable feature of this solution is its capacity for processing large amounts of data."

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

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."

"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."

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

More Apache Spark pros

"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."

"The solution is scalable."

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

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."

"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."

"Apache Spark should add some resource management improvements to the algorithms."

"The initial setup was not easy."

"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."

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

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"It's not easy to install."

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."

"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."

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

Pricing and Cost Advice

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"It is an open-source platform. We do not pay for its subscription."

"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."

"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."

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

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

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

"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."

More Apache Spark pricing and cost advice

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

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?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

See all answers

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 26% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

More Apache Spark Competitors

No data available

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

April 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

April 2025

Free Report: Apache Spark vs. npm

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

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