Focuses on big data, data analysis, data warehousing, data middle platform, data science, Flink, AI and interview experience, side‑hustle earning and career planning.
This guide explains why data warehouses need a four‑layer architecture, defines the exact responsibilities and processing rules for ODS, DWD, DWS, and ADS, provides an end‑to‑end e‑commerce example, and lists common pitfalls with practical solutions.
This guide walks data engineers through the entire process of creating clear, production‑ready data architecture diagrams—from identifying the diagram type and defining layers, to selecting tools, drawing step‑by‑step components, applying visual standards, avoiding common pitfalls, and validating the final design for stakeholders.
This article provides a comprehensive, business‑driven analysis of the Data Warehouse Service (DWS) layer, covering its core positioning, design goals, modeling and aggregation tactics, storage optimizations, typical challenges with practical solutions, and best‑practice recommendations for building efficient, cost‑effective data services.
This article provides a comprehensive guide to data warehouse modeling, covering the distinction between entities and dimensions, how to define grain and merge scope, fact integration, the special role of the DWS layer, business module and subject‑area division, and practical solutions to common modeling pitfalls.
This article analyzes the architecture, sync modes, latency, scalability, usability, and deployment aspects of four popular data synchronization solutions—Sqoop, DataX, Flink CDC, and Airbyte—and provides a practical decision tree to avoid common misuse pitfalls in enterprise data pipelines.
This article offers a step‑by‑step, experience‑driven guide to data‑warehouse modeling, covering entity vs dimension, grain alignment, fact merging, DWS layer design, business module vs subject‑area mapping, and four typical pitfalls with concrete solutions to help practitioners build robust, business‑centric warehouses.
This checklist compiles 30 actionable items covering model design, data consistency, performance, quality, metadata governance, cost efficiency, and collaboration to help data warehouse teams build trustworthy, high‑performance, and maintainable data pipelines.
This guide explains the DWD (detail) and DWS (summary) layered modeling approach combined with wide‑table driving, covering model positioning, design principles, concrete schema examples, implementation techniques, performance tips, and common pitfalls to help build clean, reusable, high‑performance enterprise data warehouses.
This article explains why many enterprises suffer from mismatched "Customer ID" fields, clarifies the distinction between data modeling (the process) and a data model (the result), and walks through conceptual, logical, and physical modeling as well as three common modeling approaches—normative, dimensional, and entity—highlighting their purposes, steps, and trade‑offs.
This article presents a concise overview of ten popular open‑source data visualization tools—including Echarts, D3.js, Grafana, Plotly, Redash, Metabase, Superset, Kibana, AntV, and Pyecharts—highlighting their main features, typical use cases, and visual examples to help readers choose the right solution for their needs.
