Getting Started with Implementation Studio

Welcome to Implementation Studio! This guide will help you get started with the platform.

Prerequisites

Required Tools

• Terraform >= 1.5
• kubectl (latest stable)
• Helm 3.x
• Docker (for local testing)
• Kind (for local Kubernetes clusters)
• gcloud CLI (for GCP labs)

Cloud Accounts

• GCP Account with billing enabled (for GCP labs)
• AWS Account with appropriate permissions (for AWS labs)
• GitHub Account (for cloning and contributing)

Note: Many labs support Kind (local Kubernetes) for zero-cost learning. Cloud accounts are optional.

Installation

Install Terraform

# macOS
brew install terraform

# Linux
# Download from https://www.terraform.io/downloads

Install kubectl

# macOS
brew install kubectl

# Linux
# Follow: https://kubernetes.io/docs/tasks/tools/install-kubectl-linux/

Install Helm

# macOS
brew install helm

# Linux
# Follow: https://helm.sh/docs/intro/install/

Install Kind

# macOS
brew install kind

# Linux
# Follow: https://kind.sigs.k8s.io/docs/user/quick-start/#installation

Install gcloud CLI (for GCP labs)

# macOS
brew install google-cloud-sdk

# Linux
# Follow: https://cloud.google.com/sdk/docs/install

Install aws CLI (for AWS labs)

# macOS
brew install awscli

# Linux
# Follow: https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html

Quick Start

1. Clone the Repository

git clone https://github.com/WBHankins93/implementation-studio.git
cd implementation-studio

2. Choose Your First Lab

We recommend starting with Lab 02: Air-Gapped Deployment because:

• Fully testable locally (no cloud costs)
• Teaches critical skills for enterprise deployments
• No cloud account required

Alternatively:

• Lab 01: Standard Deployment - Supports GCP or AWS
• Lab 05: POC Sprint - Supports Kind (local), GCP, or AWS

3. Follow the Lab Instructions

Each lab includes:

• Learning objectives
• Step-by-step instructions
• Validation steps
• Troubleshooting guide

Navigate to the lab directory and follow the README.md:

cd labs/02-airgapped-deployment
cat README.md

Learning Paths

See Learning Paths for recommended progression through the labs.

Next Steps

1. Read Learning Paths to choose your path
2. Read Reference Application to understand Argo Workflows
3. Review Lab Specifications to see all available labs
4. Choose your first lab and get started!

Getting Help

• Check the lab's troubleshooting section
• Review Testing Strategy
• Open an issue on GitHub
• Check existing issues for solutions

Next Steps

1. Read Reference Application to understand what we're deploying
2. Review Testing Strategy to understand validation
3. Choose your first lab and begin!

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Happy learning!