This guide presents a systematic approach to improve data warehouse modeling by enhancing model completeness, increasing reuse through unified dimensions and services, and enforcing naming and governance standards, supported by quantitative goals, practical strategies, and real‑world case studies.
This article provides an in-depth overview of metadata concepts, their technical and business classifications, value in data management, applications such as data profiling and lineage, optimization techniques for compute and storage, lifecycle management, and comprehensive data quality assurance practices within large‑scale big data environments.
This article presents the design, implementation, and evaluation of X‑monitor, an AI‑driven, adaptive, multi‑modal financial data quality monitoring platform that combines rule‑based and self‑learning strategies to improve detection efficiency, accuracy, and flexibility for large‑scale securities‑firm data streams.
