How do you handle large volumes of unstructured data in cloud-native environments?

In cloud-native environments, unstructured data refers to data without predefined formats such as text, images, or videos. Its importance lies in its massive volume and diversity, which support key applications like AI and real-time analytics in scenarios such as media and e-commerce.

Core components include object storage (e.g., AWS S3 or MinIO) for efficient and persistent data storage, data processing frameworks (e.g., Spark or Flink) deployed on Kubernetes clusters to enable elastic scaling, and service meshes (e.g., Istio) for data flow management. Features include automated scaling, high availability, and cloud-native API integration. The practical impact is accelerated data processing and support for large-scale AI model training such as recommendation systems.

Implementation steps include: first, storing data in object storage; second, deploying containerized processing pipelines (e.g., managing Spark jobs via K8s Operators); and finally, integrating AI tools (e.g., TensorFlow) to extract value. Typical scenarios include real-time log analysis or medical image processing, with business value being improved insight efficiency and cost reduction by up to 30%.