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."The main feature that we find valuable is that it is very fast."
"I feel the streaming is its best feature."
"The data processing framework is good."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"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."
"Apache Spark provides a very high-quality implementation of distributed data processing."
"The most valuable feature of Apache Spark is its ease of use."
"The solution is very stable."
"The product's most valuable feature is dependency installation."
"The reversal build, gendered build, migrated PCA, and CT features are excellent."
"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 most valuable feature of NPM is to trigger APMs."
"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."
"The solution’s integration with other platforms should be improved."
"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."
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."
"I would like to see integration with data science platforms to optimize the processing capability for these tasks."
"The solution needs to optimize shuffling between workers."
"It requires overcoming a significant learning curve due to its robust and feature-rich nature."
"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."
"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."
"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."
Apache Spark is ranked 2nd in Java Frameworks with 60 reviews while npm is ranked 5th in Java Frameworks with 5 reviews. Apache Spark is rated 8.4, while npm is rated 8.8. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of npm writes "User friendly, easy work flow, with fast deployment after compatibility check". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Cloudera Distribution for Hadoop, whereas npm is most compared with Amazon Corretto. 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.
