What you’ll learn
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Coding Production Grade Data pipelines by Mastering Airflow through Hands-on Examples
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How to Follow Best Practices with Apache Airflow
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How to Scale Airflow with the Local, Celery and Kubernetes Wxecutors
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How to Set Up Monitoring with Elasticsearch and Grafana
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How to Secure Airflow with authentication, crypto and the RBAC UI
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Core and Advanced Concepts with Pros and Limitations
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Mastering DAGs with timezones, unit testing, backfill and catchup
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Organising the DAG folder and keep things clean
What you will learn in the course:
- Fundamentals of Airflow are explained such as what is Airflow, how the scheduler and the web server works
- The Forex Data Pipeline project is incredible way to discover many operators in Airflow and deal with Slack, Spark, Hadoop and more
- Mastering your DAGs is a top priority and you will be able to play with timezones, unit testing your DAGs, how to structure your DAG folder and much more
- Scaling Airflow through different executors such as the Local Executor, the Celery Executor and the Kubernetes Executor will be explained in details. You will discover how to specialise your workers, how to add new workers, what happens when a node crashes.
- A Kubernetes cluster of 3 nodes will be set up with Rancher, Airflow and the Kubernetes Executor in local to run your data pipelines.
- Advanced concepts will be shown through practical examples such as templatating your DAGs, how to make your DAG dependent of another, what are Subdags and deadlocks, and more.
- You will set up a Kubernetes cluster in the cloud with AWS EKS and Rancher in order to use Airflow along with the Kubernetes Executor
- Monitoring Airflow is extremely important! That’s why you will know how to do it with Elasticsearch and Grafana.
- Security will be also addressed in order to make your Airflow instance compliant with your company. Specifying roles and permissions for your users with RBAC, Prevent from accessing the Airflow UI with authentication and password, data encryption and more.
Who this course is for:
- Data Engineers
- Inspiring Data Engineers
- DevOps
- Software Engineers
- Data Scientists
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