install AWS cli : click_here
chocolatey :
- run poweshell admin and excute below cmd
Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
choco install awscli -y
AWS S3: Amazon S3 is an object storage service.
Key Concepts
- Buckets: globally unique
- Objects: files
- Folders: logical structure
Access
- Private (default)
- Public via policies or ACL
Amazon S3 Bucket Types
Amazon S3 (Simple Storage Service) provides different bucket types to support diverse workloads. Each type is optimized for specific use cases.
General Purpose Buckets
- Description: Standard S3 buckets for everyday storage needs.
- Use Cases:
- File storage (documents, images, videos).
- Backups and archives.
- Hosting static websites.
- Key Features:
- Extremely durable (99.999999999%).
- Scales automatically.
- Supports versioning, replication, lifecycle policies.
Directory Buckets
- Description: Buckets with hierarchical namespace, similar to a file system.
- Use Cases:
- Applications requiring folder-like structures.
- Enterprise file shares.
- Key Features:
- Organizes objects into directories/subdirectories.
- Easier navigation for workloads expecting folder semantics.
- Supports POSIX-like operations.
Table Buckets
- Description: Buckets optimized for tabular data storage and queries.
- Use Cases:
- Structured datasets (CSV, Parquet, JSON).
- Analytics with Athena or Redshift Spectrum.
- Key Features:
- Schema-aware storage.
- Efficient columnar queries.
- Enables filtering and scanning of large datasets.
Vector Buckets
- Description: Buckets specialized for storing vector embeddings.
- Use Cases:
- Machine learning and AI workloads.
- Semantic search and recommendation systems.
- Storing embeddings from ML models.
- Key Features:
- Optimized for similarity search (nearest neighbor queries).
- Supports high-dimensional vector storage.
- Integrates with ML frameworks and vector databases.
Summary storage Buckets
- General Purpose: Best for everyday storage.
- Directory: Ideal for workloads needing folder-like organization.
- Table: Tailored for structured, queryable datasets.
- Vector: Built for AI/ML applications involving embeddings and similarity search.
index.html
<!-- index.html -->
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>My S3 Static Website</title>
<style>
body {
font-family: Arial, sans-serif;
background: #f4f4f9;
text-align: center;
padding: 50px;
}
h1 {
color: #2c3e50;
}
p {
color: #555;
}
a {
color: #3498db;
text-decoration: none;
}
</style>
</head>
<body>
<h1>Welcome to My S3 Website</h1>
<p>This site is hosted on <strong>Amazon S3</strong> as a static website.</p>
<p><a href="error.html">Test error page</a></p>
</body>
</html>
error.html
<!-- error.html -->
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Error - Page Not Found</title>
<style>
body {
font-family: Arial, sans-serif;
background: #fff3f3;
text-align: center;
padding: 50px;
}
h1 {
color: #e74c3c;
}
p {
color: #333;
}
a {
color: #3498db;
text-decoration: none;
}
</style>
</head>
<body>
<h1>Oops! Page Not Found</h1>
<p>The page you are looking for doesn’t exist.</p>
<p><a href="index.html">Go back to Home</a></p>
</body>
</html>
