cloud-storage-optimization

Cloud Storage Optimization

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Optimize cloud storage costs and performance across multiple cloud providers using compression, intelligent tiering, data partitioning, and lifecycle management. Reduce storage costs while maintaining accessibility and compliance requirements.

When to Use

• Reducing storage costs
• Optimizing data access patterns
• Implementing tiered storage strategies
• Archiving historical data
• Improving data retrieval performance
• Managing compliance requirements
• Organizing large datasets
• Optimizing data lakes and data warehouses

Quick Start

Minimal working example:

# Enable Intelligent-Tiering
aws s3api put-bucket-intelligent-tiering-configuration \
  --bucket my-bucket \
  --id OptimizedStorage \
  --intelligent-tiering-configuration '{
    "Id": "OptimizedStorage",
    "Filter": {"Prefix": "data/"},
    "Status": "Enabled",
    "Tierings": [
      {
        "Days": 90,
        "AccessTier": "ARCHIVE_ACCESS"
      },
      {
        "Days": 180,
        "AccessTier": "DEEP_ARCHIVE_ACCESS"
      }
    ]
  }'

# Analyze storage usage
aws s3api list-bucket-metrics-configurations --bucket my-bucket

# Enable S3 Select for cost optimization
aws s3api put-bucket-metrics-configuration \
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide	Contents
AWS S3 Storage Optimization	AWS S3 Storage Optimization
Data Compression and Partitioning Strategy	Data Compression and Partitioning Strategy
Terraform Multi-Cloud Storage Configuration	Terraform Multi-Cloud Storage Configuration
Data Lake Partitioning Strategy	Data Lake Partitioning Strategy

Best Practices

✅ DO

• Use Parquet or ORC formats for analytics
• Implement tiered storage strategy
• Partition data by time and queryable dimensions
• Enable versioning for critical data
• Use compression (gzip, snappy, brotli)
• Monitor storage costs regularly
• Implement data lifecycle policies
• Archive infrequently accessed data

❌ DON'T

• Store uncompressed data
• Keep raw logs long-term
• Ignore storage optimization
• Use only hot storage tier
• Store duplicate data
• Forget to delete old test data