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Update README.md
amanda-tan committed Sep 9, 2024
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# Workshop Overview

In this workshop, you will learn how to set up and manage a containerized research workflow with Extract, Load, Transform (ELT) features and a user interface (UI) to view and analyze outputs. We will review containerization fundamentals, ELT processes, and create an intuitive UI for end users.
In this workshop, you will learn how to set up and manage to create containerized research workflows for scalability and reproducibility. You will work with the NOAA Global Surface Summary of the Day (GSOD) dataset from the AWS Open Registry. Within the container, you will learn to clean and manipulate the dataset to work with databases and create a web-based user interface (UI) to view and analyze outputs. Along the way, you will learn best practices and standards for creating containers that are replicable across multiple computing systems.

## Course Level

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By the end of this workshop, you will be able to:

- Understand the process to implement containers for a workshop
- Set up a containerized environment for research workflows
- Implement data processing
- Containerize a user-friendly interface for accessing output and visualization
- Understand the process to implement containers for a scalable research workflows
- Set up a containerized environment for reproducible and replicable science
- Implement large-scale data processing within containers
- Create a user-friendly interface for accessing output and visualization within the container
- Learn how to use cloud tools to ochestrate and scale your containers
- Learn how to share your containers on free or paid container registries


## Delivery Method

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## Duration

- 5 Hours
- 4 Hours

## Workshop Outline

This workshop covers the following concepts:

1. **Introduction to Containerized Research Workflows**
- Overview of Docker and Kubernetes
- Benefits of containerizing research workflows
- Tools and technologies: Docker, Kubernetes, etc.

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- Creating visualization dashboards

6. **Deploying to a Public Cloud as a Kubernetes Cluster**
- Setting up a Kubernetes cluster on a public cloud (e.g., AWS, Google Cloud, Azure)
- Setting up a Kubernetes cluster on a public cloud (for this course we will be using GCP)
- Deploying containerized applications to Kubernetes
- Managing and scaling applications in Kubernetes

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## Hands-On Lab Exercises

1. Setting up Docker containers
2. Deploying and managing the complete workflow
3. Deploying the workflow to a Kubernetes cluster on a public cloud
2. Create a web-based UI using Flask
3. Deploying and managing the complete workflow
4. Deploying the workflow to a Kubernetes cluster on a public cloud

By the end of this workshop, participants will have a comprehensive understanding of how to build and manage a containerized research workflow using Docker and/or Kubernetes.
By the end of this workshop, participants will have a comprehensive understanding of how to build and manage a containerized research workflow using Docker and/or Kubernetes.

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