This article explains the concept of Slowly Changing Dimensions (SCD) in data warehouses, compares six common SCD handling methods—including SCD0, SCD1, SCD2, SCD3, combined SCD2+SCD3, and historical tables—and guides you on selecting the most suitable approach for your business needs.
This article analyzes why data engineers face endless data‑retrieval requests, identifies common missteps in data‑construction such as demand‑driven development, lack of modeling and OLAP concepts, and proposes a dimension‑model‑based data warehouse with OLAP reporting, tooling, and knowledge‑empowerment to break the cycle.
The article presents a comprehensive design of an end‑to‑end data warehouse for information‑security governance, detailing background motivations, multi‑layer data architecture, dimension modeling, bus‑matrix mapping, real‑time (lambda/kappa) processing, data‑dictionary integration, and future directions toward unified streaming‑batch solutions.
This article presents a JD.com case study on applying Flink SQL for real‑time dimension modeling, detailing two complex streaming scenarios—full‑join of multiple streams and full‑group aggregation—along with the associated challenges of historical data handling, state management, and performance optimization, and proposes component‑based architectural solutions.
This article presents JD's real-time dimension modeling case using Flink SQL, detailing two complex streaming scenarios, the difficulties of handling historical data and state management, and a component‑based solution that leverages external KV stores and optimized Flink operators to improve performance and scalability.
This article presents a JD.com BI engineer's case study on applying Flink SQL to real‑time dimension modeling, detailing two complex streaming scenarios, the technical difficulties of handling historical data and performance, and a component‑based solution architecture with future roadmap considerations.
This article explains how data-driven decision making evolves from simple Excel and Python analysis to robust data warehouses, introduces star‑schema dimension modeling, and demonstrates practical integration of Alibaba Cloud MaxCompute with Tableau for interactive OLAP‑style analytics.
This article explains why real‑time data warehouses are needed, outlines their core principles, compares them with offline warehouses, describes typical use cases such as real‑time OLAP, dashboards, feature generation and monitoring, and provides a step‑by‑step guide to designing, implementing, and operating a Flink‑based streaming warehouse with Kafka, HBase, and metadata management.
