A.B.C.D Apache Airflow 2.x on AWS - EKS | Minikube | Helm
What you'll learn
- Create your own Helm Chart
- Deploy Airflow on AWS EKS
- Create a Scalable Cluster
- Create Pipeline for Deployment
- Configure airflow using templates
- Little Knowledge around Shell/ Python Scripting
- Basic Knowledge about Kubernetes
- Basic Knowledge about Airflow Components
In this course, you will learn how to:
Create a Script to seamlessly deploy Airflow for Local Development
Create your own Helm Chart
Template Scripts and YAML files
Configure Airflow using Helm
Create a Scalable EKS Cluster using eksctl
Deploy an ALB Ingress Controller for Load Balancing and accessing the Airflow UI
Mounting EFS for Persisting Kubernetes Executor Logs
Creating a Pipeline to Deploy Airflow using AWS Code Pipeline
Basic Knowledge about Airflow Components
Basic Knowledge about Kubernetes
Basic Knowledge about AWS
Familiar with working on an IDE
Dependencies: Linux OS, Windows, AWS Account (For Non-Local Deployment )
This course is lined up with the Production Guide of Apache Airflow to deploy a Highly Scalable Airflow on EKS and also follows official Documentation of AWS while deploying Services making sure you always stay up to date and acquire more detailed information whenever you want to.
Who this course is for:
If you are a DevOps Engineer and want to know the technical dependencies for deploying Airflow such as using EFS for using a Kubernetes Executor.
If you are a Data Engineer and want to use Airflow for Development but don't want to spend a huge amount of time learning how to configure it.
If you are a Full Stack Engineer and want to learn about various frameworks revolving around Airflow such as Helm, AWS EKS.
If you want to focus on development and get rid of all the frustration coming from trying to set up Airflow with all the core components.
Who this course isn't for: If you want to know what Airflow is or learn how to create DAGs or pipelines.
Note: This course includes using AWS resources such as EKS which is not free tier eligible.
Who this course is for:
- Airflow beginners who wants to deploy it in a few commands
- Devops, Platform Engineers
- Data Engineers looking to leverage scalability of Airflow
M Tech educated Full Stack Developer with 4+ Y.O.E
Assumed various roles such as Embedded/ IoT Engineer, Python Developer, and Technical Analyst dealing with a wide variety of frameworks including Flask, Airflow, TensorFlow, Kubernetes, AWS, and the likes.
Discovering uncharted solutions. Enjoys Training and Creating Technical Content.
Pure Optimist | Former Robot | Snowboarder