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 8.1, while GitHub is ranked #4 with an average rating of 10.0. Apache holds a 11.8% mindshare in JF, compared to GitHub’s 3.7% 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 69 Apache Spark reviews

7,446 Views
1,489 Comparison Views

90% willing to recommend

npm

Read 6 npm reviews

570 Views
564 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: August 2026).

Buyer's Guide

Apache Spark vs. npm

August 2026

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Helped 909,725 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 (6th)

npm

Ranking in Java Frameworks

4th

Average Rating

9.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Java Frameworks category, the mindshare of Apache Spark is 11.8%, up from 8.1% compared to the previous year. The mindshare of npm is 3.7%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Java Frameworks Mindshare Distribution
Product	Mindshare (%)
Apache Spark	11.8%
npm	3.7%
Other	84.5%

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

"The fault tolerant feature is provided."

"Machine learning, real time streaming, and data processing are fantastic, as well as the resilient or fault tolerant feature."

"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."

"I found the solution stable. We haven't had any problems with it."

"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."

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

"The product's deployment phase is easy."

More Apache Spark pros

"There are a lot of features that are very fast in npm, even though it was developed 10 or 12 years back."

"The solution is scalable, and approximately ten users are utilizing this solution in our organization."

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

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

"NPM is user-friendly, has a smooth workflow, and has great security."

"It is a requirement for us to work with NPM for our APM to run, we need to have that NPM as a base."

"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."

"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."

More npm pros

Cons

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

"They currently use a JDK version which is a little bit old. Not all features are on it."

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

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

"Very often in many of my experiments, the data set has had to be partitioned, and there have been issues in handling very large data sets, with most of my work done using Python machine learning libraries, requiring chunking, and speed of prediction has been an issue of concern in some experiments where we have had to shut down processes due to CPU requirements, then restart with different Apache configurations, and resourcing support is a major determinant if I were to name a constraint in terms of running machine learning experiments."

"I do not know exactly what was the reason to move away from Apache Spark or the underlying database system, but it was simply a decision driven by the customer."

"It is useful for scientific purposes, but for commercial use of big data, it gives some trouble."

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

More Apache Spark cons

"The product can be improved by updating the libraries. Previously, libraries were missing, and we had difficulty building them."

"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."

"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."

"NPM can improve the package manager."

"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 only room for improvement for npm is their audit capabilities; otherwise, I am satisfied with the solution."

"The library could be updated."

Pricing and Cost Advice

"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 product is expensive, considering the setup."

"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."

"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."

"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."

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

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

More Apache Spark pricing and cost advice

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

"NPM is an open-source solution."

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

20%

Construction 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	16
Large Enterprise	33

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

Questions from the Community

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?

I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...

See all answers

What is your primary use case for Apache Spark?

I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...

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What needs improvement with NPM?

Audit capabilities should be improved. Regarding npm, I think that's all I remember for anything else I would like to add or improve. The only room for improvement for npm is their audit capabiliti...

See all answers

What is your primary use case for NPM?

I have experience in both GitHub and JMeter. I used JMeter in the earlier phase of my career for performance testing for around two to three years, and with GitHub, I have been using it almost ever...

See all answers

What advice do you have for others considering NPM?

I can talk about BrowserStack regarding the features I find most valuable in LambdaTest. npm is a 10 out of 10 for me. I am currently working as a Senior QA Automation Engineer for Cosm. My overall...

See all answers

Comparisons

AWS Lambda vs Apache Spark

Compared 7% of the time

Amazon EC2 vs Apache Spark

Compared 7% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

Apache NiFi vs Apache Spark

Compared 5% of the time

AWS Batch vs Apache Spark

Compared 5% of the time

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Spring MVC vs npm

Compared 30% of the time

Amazon Corretto vs npm

Compared 27% of the time

Spring Boot vs npm

Compared 25% of the time

Jakarta EE vs npm

Compared 18% of the time

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Product Reports

Buyer's Guide

Apache Spark

August 2026

Download Apache Spark product report

Buyer's Guide

Java Frameworks

August 2026

Download npm product report

Overview

Apache Spark is a leading open-source processing tool known for scalability and speed in managing large datasets. It supports both real-time and batch processing and is widely used for building data pipelines, machine learning applications, and analytics.

Apache Spark's strengths lie in its ability to process large data volumes efficiently through real-time and batch capabilities. With in-memory computation, it ensures fast data processing and significant performance gains. Its wide range of APIs, including those for machine learning, SQL, and analytics, make it versatile in handling complex data operations. While popular for ease of use and fault tolerance, Spark's management, debugging, and user-friendliness could benefit from improvements. Better GUIs, integration with BI tools, and enhanced monitoring are desired, alongside shuffling optimization and compatibility with more programming languages.

What are Apache Spark's key features?

Scalability: Efficiently manages large datasets across nodes.
Performance: In-memory computation for faster data processing.
Real-time Processing: Supports real-time analytics and data streaming.
APIs: Offers extensive APIs for machine learning, SQL, and analytics.

What benefits or ROI should users look for in reviews?

Ease of Use: Simplifies complex data tasks through intuitive operations.
Fault Tolerance: Ensures data reliability and continuous operations.
Integration Flexibility: Easily integrates with big data platforms and tools.

Organizations use Apache Spark predominantly for in-memory data processing, enabling seamless integration with big data frameworks. It's applied in security analytics, predictive modeling, and helps facilitate secure data transmissions in AI deployments. Industries leverage Spark's speed for sentiment analysis, data integration, and efficient ETL transformations.

Apache

npm is a user-friendly package manager known for its smooth workflow and robust security. It is embraced by developers for its open-source and scalable nature, ideal for integrating features and managing dependencies efficiently.

npm stands out as a crucial tool for developers, aiding in building code and managing libraries from GitHub. It helps create APIs for microservices, orchestrate dashboard tasks, and manage development servers effortlessly. With npm, developers can compile Node.js applications and run JavaScript code, enhancing automation via npm commands in CI/CD pipelines. Although some developers prefer Cloudsmith for library management, npm remains a staple in dependency installation and supports seamless integration with platforms like GitHub Actions.

What are npm's key features?

User-friendly interface ensures smooth workflows and great security.
Reversal build and gendered build options enhance custom development.
Migrated PCA and CT functionalities support advanced analytics.
Open-source and scalable, suitable for both individual and organizational use.
Integration with GitHub Actions and other tools promotes seamless feature integration.

What benefits or ROI can be evaluated?

Enhances automation in development processes through npm commands in CI/CD pipelines.
Supports efficient dependency management, saving time and resources.
Facilitates seamless integration, reducing management time for packages.
Improves security by managing and updating libraries with open-source capabilities.

npm is widely implemented in software development industries for building and managing JavaScript projects efficiently. It plays a crucial role in automating microservices deployment, orchestrating tasks in development environments, and managing scalable Node.js applications. Supporting a wide range of integrations, it is invaluable in projects that prioritize CI/CD pipeline automations.

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

August 2026

Free Report: Apache Spark vs. npm

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