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 5.5% 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 65 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

Download the report

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: March 2025).

Buyer's Guide

Apache Spark vs. npm

March 2025

Download the complete report

Helped 845,406 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 April 2025, in the Java Frameworks category, the mindshare of Apache Spark is 5.5%, down from 7.5% 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.

Read full review

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.

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

"The processing time is very much improved over the data warehouse solution that we were using."

"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."

"Provides a lot of good documentation compared to other solutions."

"The product’s most valuable features are lazy evaluation and workload distribution."

"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."

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

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"The scalability has been the most valuable aspect of the solution."

More Apache Spark pros

"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."

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

Cons

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

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"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."

"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."

"It should support more programming languages."

"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."

"Dynamic DataFrame options are not yet available."

"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."

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."

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

"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 library could be updated."

"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."

Pricing and Cost Advice

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

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

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

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

"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."

"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."

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."

See which vendors are best for you

Use our free recommendation engine to learn which Java Frameworks solutions are best for your needs.

See recommendations

845,406 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

28%

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 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% 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

March 2025

Download Apache Spark product report

Buyer's Guide

Java Frameworks

March 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

March 2025

Free Report: Apache Spark vs. npm

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

DOWNLOAD NOW

845,406 professionals have used our research since 2012.

See our Apache Spark vs. npm report.

See our list of best Java Frameworks vendors.

We monitor all Java Frameworks reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.