Course Number: PYTH-222

Duration: 2 days (13 hours)

Format: Live, hands-on

Mastering Airflow Training Overview

This Advanced Airflow training course goes beyond the fundamentals to teach attendees how to create more sophisticated DAGs (Directed Acyclic Graphs) and apply security practices to Apache Airflow. In addition, students learn how to scale Airflow within Kubernetes.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

Objectives

  • Secure your Apache Airflow installation
  • Create highly concurrent DAGs in Kubernetes
  • Leverage most of the new functionality Airflow 2.x brings

Prerequisites

All students should have taken Introduction to Apache Airflow or have the equivalent knowledge.

Outline

Expand All | Collapse All

Introduction
Creating DAGs
  • Secrets, connections, and variables
  • Creating connections on startup
  • Using Pools for long-running and demanding tasks
  • Simulating long-running tasks
  • DAG serialization
  • DAG versioning
  • Testing DAGs
  • CI/CD in Airflow
Modularizing DAGs
  • TaskGroups vs subDAGs
  • TaskFlowAPI and XComs
  • Modularizing
  • Dynamic and Functional DAGs
  • SmartSensors and timeouts
Airflow Security
  • RBAC in Airflow
  • Setting up OAuth authentication
  • Add Google OAuth
  • Adding SSL certs
  • Default Roles and custom roles
  • Creating a custom role
Airflow in Kubernetes
  • The Helm chart
  • Deploying Airflow with Helm
  • Deploying single tasks to Kubernetes: KubernetesPodOperator
  • Adding a task in Kubernetes
  • Scaling Airflow with Kubernetes executor
  • Changing the Helm charts values
  • KEDA autoscaler
  • Preparing DAGs for Kubernetes
  • Creating a DAG fully in Kubernetes
  • The CeleryKubernetes executor for extreme scalability
Upgrading from Airflow 1.10
Conclusion

Training Materials

All Airflow training students receive comprehensive courseware.

Software Requirements

  • Python 3.5 or later
  • Airflow 2.1 or later


Learn faster

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Satisfaction guarantee

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