Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Apache Airflow 2.0 : Complete Distributed Configuration
Rating: 4.2 out of 5(32 ratings)
212 students

Apache Airflow 2.0 : Complete Distributed Configuration

Setup HA Airflow using multiple Schedulers and Celery Workers
Last updated 4/2021
English

What you'll learn

  • Gain complete understanding of the apache airflow
  • Learn different types of Executors and it's working principles
  • Own a scalable distributed airflow setup which could be shared by multi teams in your organisation

Course content

6 sections37 lectures2h 20m total length
  • Introduction3:11
  • Motivation0:44

Requirements

  • Python >= 2.7

Description

Airflow is a platform created by the community to programmatically author, schedule and monitor workflows. Airflow is one of the best open source orchestrators and it is used widely because it is simplicity, scalability and extensibility.

Main goal of this course is to achieve an Airflow distributed setup using Celery Executor and be able to run more than 100 jobs or DAGs in parallel at any instance in time. I cover Sequential, Local and Celery Executor in this course. We acquire few EC2 instances from AWS and configure these executors.

Airflow community recently released Airflow 2.0. It contains many amazing features like HA Scheduler, Massive performance improvement on scheduler as well as celery workers etc. I myself fascinated by airflow 2.0 performance and migrated all airflow 1.x to 2.x in my organisation.

I am adding a new module on apache airflow 2.0. Here, we begin the module by learning new enhancements and HA architecture of airflow 2.0. Next, we install Webserver, Scheduler, Celery workers and Flower components. At the end, we configure multiple schedulers and observe its performance.

In addition to this, we explore salient features like Login, Email alerting and Logs management.

By the end of this course, you own a great distributed airflow setup which could be shared by multi teams in your organisation.

Who this course is for:

  • Curious on Big Data Technologies