How do cloud-native environments handle large-scale data storage?

Cloud-native environments support elastic scaling through containerization and microservice architecture, enabling dynamic resource management. Massive data storage requires efficient processing of huge volumes of distributed data, whose importance lies in supporting high availability and real-time analysis, widely applied in scenarios such as AI/ML training, IoT data processing, and large-scale log storage.

Core components include distributed storage systems (such as object storage S3 or block storage), data services (such as NoSQL database Cassandra), and Kubernetes persistent volumes (such as PersistentVolumeClaims). Features involve auto-scaling, failure recovery, and fault tolerance, with principles relying on microservice isolation and container orchestration. In practical applications, it improves data access speed, reduces latency, supports the construction of enterprise-level data analysis platforms, and promotes digital transformation.

Processing methods: Deploy StatefulSets to manage stateful applications, integrate cloud provider storage services (such as AWS EBS or GCP Persistent Disk), and use the Operator pattern to automatically manage databases. Business values include optimizing storage costs, enhancing horizontal scalability, and simplifying operations and maintenance, with typical scenarios such as real-time stream processing and data lake architecture.