Apache Pig is recommended for data engineers and analysts who are working in Apache Hadoop environments and need to perform ETL (Extract, Transform, Load) operations on large datasets. It is also suitable for teams looking to leverage existing Hadoop infrastructures without delving into complex Java MapReduce programming or when migrating legacy processing scripts based on Pig Latin.
Based on our record, React seems to be a lot more popular than Apache Pig. While we know about 814 links to React, we've tracked only 2 mentions of Apache Pig. 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.
One inspiring example is a developer building a "Todoist Clone" using a combination of React, Node.js, and MongoDB. The developer tapped into open source libraries and community support to create a highly responsive task management application. This project underscores how indie hackers can achieve rapid development and adaptation with minimal budget – a theme echoed in several indie hacking success stories. - Source: dev.to / about 1 month ago
Next.js is a very popular framework built on top of the React.js library and it provides the best Development Experience for building applications. It offers a bunch of features like:. - Source: dev.to / about 2 months ago
Explore the official React documentation. - Source: dev.to / 2 months ago
We’ll be creating the components package inside the packages directory. In this monorepo package, we’ll be building React components which will be consumed by our Next.js application (front-end package). - Source: dev.to / 2 months ago
After evaluating our options including upgrading from AngularJS to Angular (the name for every version of Angular 2 and beyond) or migrating and rewriting our application in a completely new JavaScript framework: React. We ultimately chose to go with ReactJS. - Source: dev.to / 2 months ago
Pig, a platform/programming language for authoring parallelizable jobs. - Source: dev.to / over 2 years ago
In the early days of the Big Data era when K8s hasn't even been born yet, the common open source go-to solution was the Hadoop stack. We have written several old-fashioned Map-Reduce jobs, scripts using Pig until we came across Spark. Since then Spark has became one of the most popular data processing engines. It is very easy to start using Lighter on YARN deployments. Just run a docker with proper configuration... - Source: dev.to / over 3 years ago
Vue.js - Reactive Components for Modern Web Interfaces
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
Next.js - A small framework for server-rendered universal JavaScript apps
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Svelte - Cybernetically enhanced web apps
Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)