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 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.
This article compiles the full set of first, second, and third‑round interview questions used by ByteDance for a data warehouse engineer role, covering topics such as window functions, data skew, shuffle mechanisms, data modeling, data quality, governance, and system design, along with interview duration and interviewer details.
This article explains the concept of Data Fabric, debunks common misconceptions, outlines the three key drivers behind its rise, and provides a practical four‑step roadmap—including metadata, semantic layers, policy engines, and AI—to help teams of any size adopt the technology.
This article outlines a comprehensive roadmap for data warehousing, covering fundamental concepts, essential big‑data tools, practical implementation steps, advanced architectural topics, and emerging trends such as cloud‑native warehouses and machine‑learning integration, helping readers build a solid knowledge base.
This article explains the purpose of the DWS (Data Warehouse Service) layer, why wide‑table modeling is crucial for performance and service‑oriented interfaces, and provides ten practical wide‑table designs with core field definitions, CREATE TABLE statements, and sample INSERT queries for common business domains such as products, users, orders, regions, channels, suppliers, services, finance, logistics, and data quality monitoring.
This article dissects the five most common pitfalls when transitioning from DWD to DWS aggregation tables—such as chimney‑style designs, over‑wide tables, grain mismatches, missing drill‑down keys, and performance neglect—and offers concrete, production‑ready solutions to create reusable, efficient, and cost‑effective data‑warehouse layers.
