Course · Intermediate
Deploying to the Cloud
You've learned cloud fundamentals and built a data pipeline locally with Docker and Airflow. Now it's time to deploy. But cloud platforms aren't interchangeable — AWS and GCP organize resources differently, use different services, and follow different patterns. This course gives you hands-on deployment experience with both. You'll deploy a complete Apache Airflow pipeline to AWS using ECS, Fargate, S3, RDS, and an Application Load Balancer. Then you'll take the same pipeline and deploy it to GCP using a Compute Engine VM and Cloud Storage — a simpler architecture that shows how the same Docker and Airflow skills transfer across platforms. By the end, you'll have deployed production-grade pipelines to both AWS and GCP, giving you the flexibility to work with whichever platform your team uses.
- Intermediate friendly
- 10 hrs
- 5 lessons
- Premium
Course overview
Deploy production Apache Airflow pipelines to both AWS and GCP using Docker, managed container services, and cloud storage.
What's inside
5 lessons
- 01 120 minDeploying Airflow to the Cloud with Amazon ECS (Part II)
Set up Airflow on AWS with S3, RDS, IAM, and ALB. Learn to build a secure, reliable, and scalable ETL pipeline beyond your local environment.
- 02 120 minDeploying Airflow to the Cloud with Amazon ECS (Part III)
Deploy Airflow to AWS with ECS Fargate. Build a custom Docker image, run workflows in the cloud, and manage tasks without local dependencies.
- 03 120 minDeploying Airflow to the Cloud with Amazon ECS (Part I)
Learn how to set up Apache Airflow with Docker locally to automate ETL workflows, manage dependencies, and streamline development.
- 04 120 minDeploying Airflow to the Cloud with Google Cloud Platform (Part II)
Learn how to integrate Google Cloud Storage with your Airflow pipeline. Set up GCS buckets, configure service accounts for authentication, add cloud upload tasks to your DAG, and deploy a complete ETL system that automatically delivers data to persistent cloud storage.
- 05 120 minDeploying Airflow to the Cloud with Google Cloud Platform (Part I)
Learn to deploy Airflow on GCP using Compute Engine and Docker. Adapt AWS project, configure VM, install Docker, transfer files, build containers, and run pipeline in cloud. Includes firewall setup, static IP, cost management, and troubleshooting.
Part of these paths
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