How do you integrate cloud-native data management with big data technologies?

Cloud-native data management leverages the agility of containerization and cloud services to efficiently handle the data lifecycle, with its importance lying in enhancing the elasticity and scalability of big data processing. Application scenarios include real-time stream analytics, AI training, and enterprise data lakes, supporting agile decision-making.

The integration core is Kubernetes orchestrating big data frameworks such as Spark to achieve resource isolation and automatic scaling, with features including integration of cloud storage (e.g., S3) and service mesh. Practical applications simplify pipeline construction, support real-time data processing, and have the impact of reducing operational costs and accelerating data analysis innovation.

Implementation steps: 1. Containerize big data tools (e.g., Spark). 2. Deploy to a Kubernetes cluster and configure storage volumes. 3. Integrate cloud services (e.g., message queues). Business values include elastic resource utilization and rapid insights, with typical scenarios being real-time financial risk control or predictive maintenance.