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Data Management and Storage

What is the role of data lakes in cloud-native data management?

A data lake is a centralized storage system used for storing raw, structured, and unstructured data, playing a key role in cloud-native data management. Its importance lies in providing a unified and scalable storage foundation, supporting large-scale data analysis and machine learning applications, and being widely used in cloud computing environments to achieve efficient data governance and agile development.

The core features of a data lake include schema-less design, low-cost storage, and support for multiple formats (such as JSON, Parquet). Combined with object storage services (e.g., AWS S3) and Kubernetes containerization, it enables elastic scalability. In practical applications, it facilitates batch processing, stream processing, and real-time analytics. Through cloud-native tools like Spark and Flink, it enhances data accessibility and reduces operational complexity, thereby driving business intelligence innovation.

The role of a data lake is to unify multi-source data, accelerate data ingestion and processing, and support AI/ML model training and real-time insights. This brings business values including optimized decision-making, reduced storage costs, and improved resource utilization efficiency and business agility through cloud-native elasticity.

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