Beginning and Advanced Airflow Overview
This Comprehensive Airflow training course teaches software engineers and data engineers the fundamental and advanced Airflow skills they need to successfully orchestrate production-ready data pipelines. Students learn how to create 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.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Create production-ready data pipelines in Airflow
- Build pipelines in Airflow that are able to scale to hundreds of tasks
- Enforce modularization and reusability of Airflow tasks across projects
- Scale Airflow in Kubernetes
- Secure your Apache Airflow installation
- Create highly concurrent DAGs in Kubernetes
- Leverage most of the new functionality Airflow 2.x brings
Prerequisites
All attendees should have basic Python knowledge or object-oriented programming experience.
Outline
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Introducing Apache Airflow
- What Airflow is and what does it solve?
- Airflow architecture
- How do we represent a Pipeline?
- Our first DAG
- Tasks, TaskFlow, and Operators
- First Pipeline
Mastering scheduling
- execution_date, start_date and schedule_interval
- Handling non-default schedule_intervals
- Playing with time
Abstracting functionality
- Using custom operators
- Creating TaskGroups vs subDAGs
- Sharing data with xCOMs
- Branching and Triggers
- Sensors and SmartSensors
Executors and Scaling Airflow
- Abandoning SQLite for PostgreSQL
- Executors: Debug, Local, Celery
- Concurrency and parallelism
- Concurrency with Celery
- Airflow in Kubernetes, the old and new ways
- KEDA and HA scheduler
- Deploying a highly availability fault-tolerant Airflow
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
