Alibaba Cloud Data Lake: Unified Metadata and Storage Management Practices
This article explains Alibaba Cloud's data lake architecture, unified metadata services, storage management optimizations, and format handling techniques, illustrating how lakehouse concepts, multi‑engine support, and lifecycle policies enable efficient, secure, and cost‑effective big data processing in the cloud.
The article introduces the growing importance of unified metadata and storage management for data lakes and outlines Alibaba Cloud's approach, covering cloud data lake architecture, lakehouse concepts, and the benefits of separating storage and compute using open formats like Parquet and ORC.
It details the unified metadata service (DLF), which provides a fully managed Hive Metastore‑compatible API, supports multiple engines (Spark, Hive, Presto, MaxCompute, Hologres, Flink), and stores metadata in a scalable table storage backend, offering multi‑versioning, fine‑grained permissions, and migration tools.
The storage management section describes the use of OSS object storage, data profiling metrics (size, access frequency, last access time), and lifecycle policies that automatically archive or tier data based on usage patterns, helping reduce costs while maintaining accessibility.
Format management discusses handling of lake formats such as Delta Lake, Hudi, and Iceberg, including automated vacuum and optimization jobs that clean up old versions and merge small files, with rule‑based triggers to maintain performance without manual intervention.
A Q&A segment compares DLF metadata management with Databricks Unity Catalog, clarifies the openness of DLF APIs, and explains current capabilities for small‑file governance and DDL listening.
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