What are the challenges of managing large datasets in cloud-native environments?

Managing large datasets in cloud-native environments faces comprehensive challenges such as storage performance, network transmission, data consistency, and security compliance. While the dynamic and distributed nature of cloud-native architectures provides elastic scalability advantages, it exacerbates the complexity of persistent storage, efficient access, and cross-node synchronization of massive data, directly impacting business effectiveness in critical scenarios like real-time analytics and AI training.

Key challenges include: 1) Difficulty balancing storage cost and performance (e.g., object storage offers high economy but high latency, while local SSDs provide fast speed but lack durability); 2) Data movement efficiency is constrained by network bandwidth, especially in cross-availability zone or hybrid cloud scenarios; 3) Challenges in maintaining distributed state consistency (e.g., data location changes caused by Kubernetes Pod drift); 4) Security policies need to cover data flows between dynamic containers (encryption, RBAC, auditing); 5) Lack of Data Locality leading to computational performance bottlenecks.

Practical responses require combining technology stacks: 1) Adopting tiered storage (object storage + distributed file systems + local volumes); 2) Deploying data orchestration engines (e.g., Fluid) to achieve cache acceleration and intelligent scheduling; 3) Using service meshes to enhance transmission security; 4) Integrating with cloud-native storage services via CSI drivers. Ultimately, this improves processing efficiency and reduces TCO, but requires continuous optimization of data placement strategies and lifecycle management.