Working Code Demo:

Today's Build Agenda

You'll implement an intelligent tiered storage system that automatically moves log data across four storage tiers based on age, access patterns, and cost optimization. This enterprise-grade feature reduces storage costs by 60-80% while maintaining query performance.

What We're Building:

Hot Tier: Recent logs (0-7 days) on high-performance SSD storage
Warm Tier: Moderately accessed logs (7-30 days) on standard storage
Cold Tier: Rarely accessed logs (30-365 days) on low-cost storage
Archive Tier: Long-term retention logs (365+ days) on tape/glacier storage

Core Concepts: Intelligent Data Lifecycle Management

Tiered storage transforms static data storage into a dynamic, cost-aware system. Instead of keeping all logs on expensive high-performance storage, data automatically migrates based on access patterns and business rules.

Key Design Principles:

Performance Degradation Acceptance: Newer logs need millisecond access, older logs can tolerate seconds
Cost Optimization: Storage costs decrease by 90% from hot to archive tiers
Transparent Migration: Applications continue working without knowing data location
Policy-Driven Movement: Business rules, not technical constraints, drive data movement

Real-world impact: Netflix saves millions annually by moving viewing logs from expensive NVMe storage to glacier archives after 90 days, while keeping recent data hot for recommendation algorithms.

Context in Distributed Systems

Tiered storage sits at the intersection of storage management, cost optimization, and performance engineering. In distributed log processing, it enables horizontal scaling without linear cost increases.

System Integration Points:

Query Router: Determines which tier contains requested data
Storage Controller: Manages data movement between tiers
Metadata Index: Tracks data location and access patterns
Policy Engine: Evaluates migration rules and schedules transfers

Your tiered storage integrates with previous components: authentication (Day 112) ensures only authorized users access archived data, while upcoming lifecycle policies (Day 114) will define retention rules across tiers.

\n \n Read more\n \n

","contentEncoded","freeContentEncoded","textContent","Working Code Demo:Today's Build AgendaYou'll implement an intelligent tiered storage system that automatically moves log data across four storage tiers based on age, access patterns, and cost optimization. This enterprise-grade feature reduces storage costs by 60-80% while maintaining query performance.What We're Building:Hot Tier: Recent logs (0-7 days) on high-performance SSD storageWarm Tier: Moderately accessed logs (7-30 days) on standard storageCold Tier: Rarely accessed logs (30-365 days) on low-cost storageArchive Tier: Long-term retention logs (365+ days) on tape/glacier storageCore Concepts: Intelligent Data Lifecycle ManagementTiered storage transforms static data storage into a dynamic, cost-aware system. Instead of keeping all logs on expensive high-performance storage, data automatically migrates based on access patterns and business rules.Key Design Principles:Performance Degradation Acceptance: Newer logs need millisecond access, older logs can tolerate secondsCost Optimization: Storage costs decrease by 90% from hot to archive tiersTransparent Migration: Applications continue working without knowing data locationPolicy-Driven Movement: Business rules, not technical constraints, drive data movementReal-world impact: Netflix saves millions annually by moving viewing logs from expensive NVMe storage to glacier archives after 90 days, while keeping recent data hot for recommendation algorithms.Context in Distributed SystemsTiered storage sits at the intersection of storage management, cost optimization, and performance engineering. In distributed log processing, it enables horizontal scaling without linear cost increases.System Integration Points:Query Router: Determines which tier contains requested dataStorage Controller: Manages data movement between tiersMetadata Index: Tracks data location and access patternsPolicy Engine: Evaluates migration rules and schedules transfersYour tiered storage integrates with previous components: authentication (Day 112) ensures only authorized users access archived data, while upcoming lifecycle policies (Day 114) will define retention rules across tiers. 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This enterprise-grade feature reduces storage costs by 60-80% while maintaining query performance.\n\n**What We're Building:**\n\n* **Hot Tier**: Recent logs (0-7 days) on high-performance SSD storage\n \n* **Warm Tier**: Moderately accessed logs (7-30 days) on standard storage\n \n* **Cold Tier**: Rarely accessed logs (30-365 days) on low-cost storage\n \n* **Archive Tier**: Long-term retention logs (365+ days) on tape/glacier storage\n \n\n* * *\n\nCore Concepts: Intelligent Data Lifecycle Management\n----------------------------------------------------\n\nTiered storage transforms static data storage into a dynamic, cost-aware system. Instead of keeping all logs on expensive high-performance storage, data automatically migrates based on access patterns and business rules.\n\n**Key Design Principles:**\n\n* **Performance Degradation Acceptance**: Newer logs need millisecond access, older logs can tolerate seconds\n \n* **Cost Optimization**: Storage costs decrease by 90% from hot to archive tiers\n \n* **Transparent Migration**: Applications continue working without knowing data location\n \n* **Policy-Driven Movement**: Business rules, not technical constraints, drive data movement\n \n\nReal-world impact: Netflix saves millions annually by moving viewing logs from expensive NVMe storage to glacier archives after 90 days, while keeping recent data hot for recommendation algorithms.\n\n* * *\n\nContext in Distributed Systems\n------------------------------\n\n[\n\n](https://substackcdn.com/image/fetch/$s_!jQ3f!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a140536-2119-4124-ad8e-192ed08c3564_691x493.png)\n\nTiered storage sits at the intersection of storage management, cost optimization, and performance engineering. In distributed log processing, it enables horizontal scaling without linear cost increases.\n\n**System Integration Points:**\n\n* **Query Router**: Determines which tier contains requested data\n \n* **Storage Controller**: Manages data movement between tiers\n \n* **Metadata Index**: Tracks data location and access patterns\n \n* **Policy Engine**: Evaluates migration rules and schedules transfers\n \n\nYour tiered storage integrates with previous components: authentication (Day 112) ensures only authorized users access archived data, while upcoming lifecycle policies (Day 114) will define retention rules across tiers.\n\n* * *\n\n[Read more](https://sdcourse.substack.com/p/day-113-implementing-tiered-storage)","freeMarkdown","kind1EventId","kind30023EventId","48d0e569c296876a4f4b0d74320eb4baae9fea3bd205a3afd880b73a4fc9c017","kind30023",{"_14":208,"_182":211,"_184":212,"_60":213,"_198":19,"_147":233,"_200":234},"1c80b82ccb03d7b24de67851baaf100317df61c7a10cc5966636d71fcd8a8851a3bd2dbd2184e6b19d572b13991cd1e2ed92384f8c7f2b06522b247a43c91415",30023,[214,217,218,221,222,224,225,228,231,232],[215,216],"r","https://drewdru.syndichain.com/articles/4cf5d8ea-3c3a-4d32-bf5d-f9d03dd31078",[24,135],[219,220],"published_at","1761124043",[27,139],[223,130],"d",[169,136],[226,227],"prev-published-event-id","f73485a3b7dab5e0b5407a649394cb7bded7faf0a86c4e2a782c12cfb025f37d",[229,230],"revision","2",[194,195],[197,19,22],"Day 113: Implementing Tiered Storage for Log Data\nWorking Code Demo:\n------------------\n\nToday's Build Agenda\n--------------------\n\nYou'll implement an intelligent tiered storage system that automatically moves log data across four storage tiers based on age, access patterns, and cost optimization. This enterprise-grade feature reduces storage costs by 60-80% while maintaining query performance.\n\n**What We're Building:**\n\n* **Hot Tier**: Recent logs (0-7 days) on high-performance SSD storage\n \n* **Warm Tier**: Moderately accessed logs (7-30 days) on standard storage\n \n* **Cold Tier**: Rarely accessed logs (30-365 days) on low-cost storage\n \n* **Archive Tier**: Long-term retention logs (365+ days) on tape/glacier storage\n \n\n* * *\n\nCore Concepts: Intelligent Data Lifecycle Management\n----------------------------------------------------\n\nTiered storage transforms static data storage into a dynamic, cost-aware system. Instead of keeping all logs on expensive high-performance storage, data automatically migrates based on access patterns and business rules.\n\n**Key Design Principles:**\n\n* **Performance Degradation Acceptance**: Newer logs need millisecond access, older logs can tolerate seconds\n \n* **Cost Optimization**: Storage costs decrease by 90% from hot to archive tiers\n \n* **Transparent Migration**: Applications continue working without knowing data location\n \n* **Policy-Driven Movement**: Business rules, not technical constraints, drive data movement\n \n\nReal-world impact: Netflix saves millions annually by moving viewing logs from expensive NVMe storage to glacier archives after 90 days, while keeping recent data hot for recommendation algorithms.\n\n* * *\n\nContext in Distributed Systems\n------------------------------\n\n[\n\n](https://substackcdn.com/image/fetch/$s_!jQ3f!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a140536-2119-4124-ad8e-192ed08c3564_691x493.png)\n\nTiered storage sits at the intersection of storage management, cost optimization, and performance engineering. In distributed log processing, it enables horizontal scaling without linear cost increases.\n\n**System Integration Points:**\n\n* **Query Router**: Determines which tier contains requested data\n \n* **Storage Controller**: Manages data movement between tiers\n \n* **Metadata Index**: Tracks data location and access patterns\n \n* **Policy Engine**: Evaluates migration rules and schedules transfers\n \n\nYour tiered storage integrates with previous components: authentication (Day 112) ensures only authorized users access archived data, while upcoming lifecycle policies (Day 114) will define retention rules across tiers.\n\n* * *\n\n[Read more](https://sdcourse.substack.com/p/day-113-implementing-tiered-storage)\nYou can include dynamic values by using placeholders like: https://drewdru.syndichain.com/articles/4cf5d8ea-3c3a-4d32-bf5d-f9d03dd31078 , Drew Dru, https://sdcourse.substack.com/p/day-113-implementing-tiered-storage , drewdru, drewdru, drewdru, drewdru These will automatically be replaced with the actual data when the message is sent.",1761219767,"publishedToNostr","htmlContentKind30023","

Working Code Demo:

Today's Build Agenda

What We're Building:

\n
Hot Tier: Recent logs (0-7 days) on high-performance SSD storage
\n
\n
Warm Tier: Moderately accessed logs (7-30 days) on standard storage
\n
\n
Cold Tier: Rarely accessed logs (30-365 days) on low-cost storage
\n
\n
Archive Tier: Long-term retention logs (365+ days) on tape/glacier storage
\n

Core Concepts: Intelligent Data Lifecycle Management

Key Design Principles:

\n
Performance Degradation Acceptance: Newer logs need millisecond access, older logs can tolerate seconds
\n
\n
Cost Optimization: Storage costs decrease by 90% from hot to archive tiers
\n
\n
Transparent Migration: Applications continue working without knowing data location
\n
\n
Policy-Driven Movement: Business rules, not technical constraints, drive data movement
\n

Real-world impact: Netflix saves millions annually by moving viewing logs from expensive NVMe storage to glacier archives after 90 days, while keeping recent data hot for recommendation algorithms.

Context in Distributed Systems

[

](https://substackcdn.com/image/fetch/$s_!jQ3f!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a140536-2119-4124-ad8e-192ed08c3564_691x493.png)

System Integration Points:

\n
Query Router: Determines which tier contains requested data
\n
\n
Storage Controller: Manages data movement between tiers
\n
\n
Metadata Index: Tracks data location and access patterns
\n
\n
Policy Engine: Evaluates migration rules and schedules transfers
\n

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