Based on our record, Parcel seems to be a lot more popular than Amazon EMR. While we know about 103 links to Parcel, we've tracked only 10 mentions of Amazon EMR. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Parcel is a fast and zero-configuration web application bundler designed to simplify the build process for modern web projects. It's not limited to web applications, and it can be used to build packages targeting the browser or Node.js. - Source: dev.to / 1 day ago
At first we wanted to just get rid of all the helper utilities. Keep only the kernel, but this would mean a loss of backward compatibility. We needed some efficient code processing instead with recomposition and tree-shaking. We needed a bundler. But which one? Our testing approach relies on targets, not sources. We rebuilt the project frequently, speed was critical requirement. In essence, we chose a solution... - Source: dev.to / 10 days ago
It runs using Parcel, very simple and easy to setup. The app has 3 files:. - Source: dev.to / 18 days ago
In the Changelog Podcast episode referenced above, Dan Abramov alluded to Parcel working on RSC support as well. I couldn’t find much to back up that claim aside from a GitHub issue discussing directives and a social media post by Devon Govett (creator of Parcel), so I can’t say for sure if Parcel is currently a viable option for developing with RSCs. - Source: dev.to / 30 days ago
Once you build a simple Vite backend integration, try not to complicate Vite's configuration unless you absolutely must. Vite has become one of the most popular bundlers in the frontend space, but it wasn't the first and it certainly won't be the last. In my 7 years of building for the web, I've used Grunt, Gulp, Webpack, esbuild, and Parcel. Snowpack and Rome came-and-went before I ever had a chance to try them.... - Source: dev.to / 3 months ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
Webpack - Webpack is a module bundler. Its main purpose is to bundle JavaScript files for usage in a browser, yet it is also capable of transforming, bundling, or packaging just about any resource or asset.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
17track - All-in-one package tracking
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
rollup.js - Rollup is a module bundler for JavaScript which compiles small pieces of code into a larger piece such as application.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost