Xiaomi Sales Data Warehouse: Architecture, Construction Theory, and Practices
This article presents a comprehensive overview of Xiaomi's sales data warehouse, covering its evolution, dimensional modeling and layer theory, Lambda architecture with batch and streaming processing, capability layers, security measures, and future trends toward real‑time metricization and data value creation.
01 Sales Warehouse Introduction: Describes the evolution of Xiaomi’s sales data warehouse, its definition, data sources, content, scale, and the overall data landscape.
02 Data Warehouse Construction Theory: Explains business analysis, dimensional modeling, layer design (ODS, DWD, DWM, DM, ADS, TMP), and modeling principles such as high cohesion, low coupling, public logic sinking, cost‑performance balance, consistency, and rollback.
03 Sales Warehouse Architecture: Introduces the Lambda architecture combining batch (Spark + Hive) and stream (Flink + Talos) processing, use of Hologres for accelerated queries, and solutions for state expiration in real‑time order processing.
04 Data Warehouse Capability Layer: Details the unified data architecture, Iceberg‑based minute‑level streaming‑batch integration, Flink‑Talos real‑time processing, data governance, security compliance, and the role of the data encyclopedia for metric management.
05 Summary and Outlook: Summarizes achievements, current usage across the company, and future trends toward data value‑creation and real‑time metrics.
06 Q&A: Provides answers to common questions about data model updates, permission layers, technology choices (Kudu, Doris, Hologres), layer definitions (DWD vs DWM), and data accessibility.
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