RSpec Reviews

Apache Airflow Reviews

RSpec mentions (26)

• Should You Use Ruby on Rails or Hanami?

  When it comes to testing code, both frameworks are very much comparable since you can test either using the versatile RSpec library. - Source: dev.to / about 1 month ago

• Test Driving a Rails API - Part Two

  When starting a Rails project, you have a lot of decisions to make. Whether or not to write tests should not be one of them. The big decision is to use Minitest or Rspec. Both of those testing frameworks are great and provide everything you need to test a Rails application thoroughly. - Source: dev.to / 3 months ago

• Should i learn testing

  As a beginner you can skip it, just focus on understanding Rails' philosophy and getting comfortable with it. However, make sure you remember to come back to unit testing later bc it's a mandatory skill for a Rails developer. Unit test can help you understand your project's specs thoroughly (assume its test coverage is more than 90%). I recommend learning RSpec instead of Rails' built-in testing tool (the one... Source: 11 months ago

• Code Reviewing a Ruby on Rails application.

  RSpec is a testing framework for Ruby that is widely used in the Ruby on Rails community. It allows developers to write and execute automated tests. RSpec promotes behavior-driven development (BDD) by providing a readable syntax for describing the expected behavior of the application. - Source: dev.to / 12 months ago

• Ruby RSpec

  In the Ruby programming language, one of the most popular testing frameworks is RSpec. RSpec is a flexible and expressive testing tool that allows you to write and run automated tests for your Ruby code. - Source: dev.to / over 1 year ago

Apache Airflow mentions (67)

• 10 Open Source Tools for Building MLOps Pipelines

  An integral part of an ML project is data acquisition and data transformation into the required format. This involves creating ETL (extract, transform, load) pipelines and running them periodically. Airflow is an open source platform that helps engineers create and manage complex data pipelines. Furthermore, the support for Python programming language makes it easy for ML teams to adopt Airflow. - Source: dev.to / 8 days ago

• AI Strategy Guide: How to Scale AI Across Your Business

  Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities. - Source: dev.to / about 1 month ago

• The 2024 Web Hosting Report

  For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules. - Source: dev.to / 4 months ago

• Best ETL Tools And Why To Choose

  Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows. Source: 7 months ago

• Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow

  Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring. - Source: dev.to / 7 months ago