3.1.1.4. Configure Storage Tiers

💡 First Principle: Aligning data storage costs with the changing value and access frequency of data over time is a fundamental strategy for financial efficiency, ensuring you pay only for the performance and accessibility you need.

Scenario: You are storing application logs in Azure Blob Storage. Logs from the last 30 days are frequently accessed for troubleshooting. Logs older than 30 days are rarely accessed but need to be retained for 1 year. Logs older than 1 year need to be archived for 7 years for compliance.

What It Is: Storage tiers are different cost/performance levels for Azure Blob Storage.

Key Access Tiers:

• Hot Tier: For frequently accessed data. Highest storage cost, but lowest access and retrieval costs.
  • Use for: Active files, current logs, media in regular use, content for high-traffic websites.
• Cool Tier: For infrequently accessed data that must remain available immediately. Lower storage cost than Hot, but higher access and retrieval costs (including read and early deletion fees).
  • Use for: Backups, older project files, compliance data, disaster recovery data.
• Archive Tier: For rarely accessed data. Lowest storage cost, but highest access cost and significant retrieval latency (hours). Data is offline and must be rehydrated before use.
  • Use for: Long-term retention, such as compliance archives or historical records where retrieval time is not critical.

Transitioning Data: Blobs can be moved between tiers manually or automatically using lifecycle management policies.

Visual: Azure Blob Storage Access Tiers and Lifecycle

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⚠️ Common Pitfall: Ignoring retrieval costs. Moving data to a cheaper storage class like Cool or Archive saves on storage costs, but frequent retrieval can make it more expensive overall than keeping it in the Hot tier due to higher per-GB retrieval fees.

Key Trade-Offs:

• Storage Cost vs. Retrieval Cost/Time: Lower storage costs (e.g., Archive) come with higher retrieval costs and longer retrieval times (hours).

Reflection Question: How does aligning storage tiers (Hot, Cool, Archive) with data access patterns (e.g., recent logs vs. archival data) fundamentally optimize storage costs and enforce data retention policies by automatically tiering or deleting data?