Optimize cloud storage across AWS S3, Azure Blob, and GCP Cloud Storage with compression, partitioning, lifecycle policies, and cost management.

Cloud Storage Optimization Table of Contents [Overview]( overview) [When to Use]( when to use) [Quick Start]( quick start) [Reference Guides]( reference guides) [Best Practices]( 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: Reference Guides Detailed implementations in the references/ directory: Guide Contents [AWS S3 Storage Optimization](references/aws s3 storage optimization.md) AWS S3 Storage Optimization [Data Compression and Partitioning Strategy](references/data compression and partitioning strategy.md) Data Compression and Partitioning Strategy [Terraform Multi Cloud Storage Configuration](references/terraform multi cloud storage configuration.md) Terraform Multi Cloud Storage Configuration [Data Lake Partitioning Strategy](references/data lake partitioning strategy.md) 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

cloud-storage-optimization