npm
Webpack
Yarn
Brunch
Grunt
GNU Make
Ender
JSPM
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
npm Amazon EMRBased on our record, npm should be more popular than Amazon EMR. It has been mentiond 61 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.
To begin, you will need to choose a name for your package. Note: Your package name must be unique. Using the exact or similar name of an existing package will return an error when publishing the package to npm. To ensure the uniquenesses of your package name, head over to npmjs.com and search for any existing packages with a similar name. If there’s an exact match or a similar name, consider changing the name... - Source: dev.to / 25 days ago
By using Fastify, you can quickly get a Node.js application up and running to handle requests. Assuming you have Node.js installed, you’ll start by initializing a new project. We’ll use npm as our package manager. - Source: dev.to / 28 days ago
It is on this last topic that I want to focus on in this post, and then in particular, how to make working with dependencies a bit safer within the NPM ecosystem. - Source: dev.to / 2 months ago
In modern applications you'll get React and React DOM files from a "package registry" like npm (react and react-dom). - Source: dev.to / 3 months ago
Install the alacritty-themes package globally with npm. - Source: dev.to / 4 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.
Yarn - Yarn is a package manager for your code.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Brunch - Brunch builds, lints, compiles, concatenates and shrinks your HTML5 app in an ultra-simple way. No more Grunt / Gulp mess.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost