How do you use cloud-native storage to optimize costs for large datasets?

Cloud-native storage leverages the elastic characteristics of cloud computing to manage storage resources with pay-as-you-go pricing, optimizing costs for large datasets. Its importance lies in reducing operational expenses and improving resource utilization, making it suitable for scenarios such as big data analytics and AI training, effectively handling PB-level data while reducing expenditures.

The core components include automatic tiering (such as hot and cold storage selection), compression, deduplication, and lifecycle management. In principle, it achieves seamless scalability through Kubernetes storage classes (e.g., AWS EBS/OSS). In practical applications, combining with CI/CD pipelines to automate data archiving reduces storage costs by 30%-50%, promoting the efficient operation of cloud-native platforms.

Implementation steps: Evaluate data access frequency; select cost-optimized storage classes (e.g., Azure Blob Cold Storage); configure S3 lifecycle rules to automatically archive old data; enable compression and monitor cost dashboards; perform regular optimization. Business values include halved storage expenditures and elastic resource scheduling.