Based on our record, Apache Airflow seems to be a lot more popular than Gauge. While we know about 68 links to Apache Airflow, we've tracked only 4 mentions of Gauge. 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.
Selenium is cool but https://gauge.org/ really cuts down on the boilerplate and is a lot more lightweight, may want to give it a look too. Source: about 1 year ago
Since the project also uses Postgres, Redis, and AMQP, we also write integration tests. A docker compose file is there to stack up the test suite, and before each test, the tables, the keys, and the queues are reset. We don't try to aim to test for all the cases but usually all the controllers are covered. I personally would prefer to write more test cases between multiple micro services (e2e?) using something... Source: over 1 year ago
Gauge looks interesting, but reminds me heavily of BDD frameworks - it looks like it's an abstraction layer where instead of writing Gherkin/GWT, the tests are in their specific DSL that's Markdown based? Source: over 2 years ago
Gauge is a Behavior Driven Java testing framework launched by ThoughtWorks.Inc. This is also one of the best Java Testing Frameworks, which allows software engineers to develop automated frameworks and speed up the software development procedure. - Source: dev.to / about 3 years ago
Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / 10 days ago
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 / 21 days ago
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 2 months ago
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
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: 8 months ago
SEOBOTS.io - Easy to use bots for data mining and growth hacking: parsers, crawlers and much more.
Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.
Wicked PDF - PDF generator (from HTML) plugin for Ruby on Rails
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
TexAu - Growth automation to scale your business faster
Make.com - Tool for workflow automation (Former Integromat)