Software Alternatives, Accelerators & Startups

Parcel VS Google Cloud Dataflow

Compare Parcel VS Google Cloud Dataflow and see what are their differences

Parcel logo Parcel

Blazing fast, zero configuration web application bundler

Google Cloud Dataflow logo Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
  • Parcel Landing page
    Landing page //
    2021-12-13
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

Parcel videos

Danby Parcel Guard Smart Mailbox blogger Review

More videos:

  • Review - PARCEL MOVIE REVIEW | SASWATA CHATTERJEE | RITUPARNA SENGUPTA | RUPAM'S REVIEW
  • Review - Le Parcel Box review

Google Cloud Dataflow videos

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Category Popularity

0-100% (relative to Parcel and Google Cloud Dataflow)
Web Application Bundler
100 100%
0% 0
Big Data
0 0%
100% 100
JS Build Tools
100 100%
0% 0
Data Dashboard
0 0%
100% 100

User comments

Share your experience with using Parcel and Google Cloud Dataflow. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Parcel and Google Cloud Dataflow

Parcel Reviews

Rollup v. Webpack v. Parcel
Parcel's caching feature sees dramatically decreases in time consumption after the initial run. For frequent, small changes, in smaller projects **Parcel*8 is a great choice.
Source: x-team.com
If you’ve ever configured Webpack, Parcel will blow your mind!
document.body.className = document.body.className.replace(/(^|\s)is-noJs(\s|$)/, "$1is-js$2")HomepageHomepageJavascriptBecome a memberSign inGet startedIf you’ve ever configured Webpack, Parcel will blow your mind!And how to hit the ground running with Parcel.Ibrahim ButtBlockedUnblockFollowFollowingMar 16, 2018Click here to share this article on LinkedIn »Zero...
Source: medium.com
First impressions with Parcel JS
The big selling point of Parcel though is that it offers a zero configuration experience. This means all the features are available out of the box! It also boasts blazing fast bundle times 👟 You won’t be configuring how Parcel works or having to draft in various plugins to get started. If you do need something, Parcel magically detects this and will pull in stuff for you on...
Source: codeburst.io
Parcel vs webpack - Jakob Lind
Parcel has made their own benchmarks of Parcel and other bundlers. Parcel has been criticized because they have not made the benchmarks open source. People cannot verify that the benchmarks are true when they are not open source.

Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Social recommendations and mentions

Based on our record, Parcel should be more popular than Google Cloud Dataflow. It has been mentiond 103 times since March 2021. 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 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 / 21 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 / 30 days 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
View more

Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 1 year ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 1 year ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 1 year ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 1 year ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 2 years ago
View more

What are some alternatives?

When comparing Parcel and Google Cloud Dataflow, you can also consider the following products

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.

rollup.js - Rollup is a module bundler for JavaScript which compiles small pieces of code into a larger piece such as application.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

17track - All-in-one package tracking

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?