Parcel Reviews

Google Cloud Dataproc Reviews

Parcel mentions (103)

• Create a typescript package with Parcel

  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 / 27 days ago

• How and why do we bundle zx?

  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 / about 1 month ago

• DEMO - Voice to PDF - Complete PDF documents with voice commands using the Claude 3 Opus API

  It runs using Parcel, very simple and easy to setup. The app has 3 files:. - Source: dev.to / about 1 month ago

• React Server Components Example with Next.js

  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 / about 2 months ago

• Build a Vite 5 backend integration with Flask

  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 / 4 months ago

Google Cloud Dataproc mentions (3)

• Connecting IPython notebook to spark master running in different machines

  I have also a spark cluster created with google cloud dataproc. Source: about 1 year ago

• Why we don’t use Spark

  Specifically, we heavily rely on managed services from our cloud provider, Google Cloud Platform (GCP), for hosting our data in managed databases like BigTable and Spanner. For data transformations, we initially heavily relied on DataProc - a managed service from Google to manage a Spark cluster. - Source: dev.to / about 2 years ago

• Data processing issue

  With that, the best way to maximize processing and minimize time is to use Dataflow or Dataproc depending on your needs. These systems are highly parallel and clustered, which allows for much larger processing pipelines that execute quickly. Source: over 2 years ago