Based on our record, Apache Airflow seems to be a lot more popular than Astronomer. While we know about 67 links to Apache Airflow, we've tracked only 4 mentions of Astronomer. 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.
A quick tip for airflow if you don't have a local install (and I heartily recommend a local install - astronomer.io has an easy to set up container). Source: over 1 year ago
Julian LaNeve is an engineer and data scientist who currently works at Astronomer.io as a Product Manager. In his free time, he enjoys playing poker, chess and winning data science competitions. - Source: dev.to / over 1 year ago
Then load up docker, don't need to be a docker expert, just install docker desktop on windows or use linux. Go to astronomer.io and look at how to run airflow (cron++) in docker. Get that working. If you don't know python but do program in some language, you should be able to get up to speed on the basics pretty quickly. If you know python, it will be a breeze. Source: over 2 years ago
Hello guys, I am currently looking for the right orchestration to build a data pipeline composed of long running tasks (python scripts) among which some run in parallel. Although I was firstly hesitating between Apache Airflow and AWS Step functions, it appeared setting Airflow for production might be too complicated without using a way too expensive service meant for that intent( aws managed worflows or... Source: about 3 years 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 / 10 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 1 month 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: 7 months ago
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 / 8 months ago
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