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3.1.5. Design for Data Archiving

šŸ’” First Principle: A cost-effective, compliant, and secure data archiving solution is essential for the long-term retention of infrequently accessed data, balancing minimal storage expenditure with defined data retrieval requirements.

Scenario: You are designing a data archival solution for a large volume of historical financial transaction records. These records are rarely accessed after 5 years but must be retained for 10 years to meet regulatory compliance. You need the most cost-effective storage with acceptable retrieval times (e.g., within 24 hours).

Data archiving ensures long-term retention of infrequently accessed data in a cost-effective, compliant, and secure manner.

Key Design Considerations:
  • Compliance & Regulatory Needs: Archiving must satisfy legal and industry mandates (e.g., GDPR, HIPAA) for data retention, privacy, and auditability.
  • Cost Optimization: Use low-cost storage tiers like Azure Blob Storage Archive, which significantly reduces expenses for rarely accessed data.
  • Data Retrieval: Archive tiers offer low storage costs but have higher latency and retrieval fees. Plan for retrieval times that may range from hours to days.
  • Data Integrity & Security: Archived data must remain unaltered. Employ encryption, immutability policies (WORM - Write Once, Read Many), and regular integrity checks.
  • Azure Services:

āš ļø Common Pitfall: Archiving data without a clear retrieval plan or budget. The cost of retrieving large amounts of data from an archive tier can be significant and unexpected if not planned for.

Key Trade-Offs:
  • Storage Cost vs. Retrieval Speed: The fundamental trade-off in archiving. The cheaper the storage, the slower and potentially more expensive the retrieval.

Reflection Question: How does designing for data archiving, by leveraging services like Azure Blob Storage's Archive tier and considering the trade-offs between storage costs and retrieval latency, fundamentally ensure long-term retention of infrequently accessed data in a cost-effective, compliant, and secure manner?