Focuses on big data, data analysis, data warehousing, data middle platform, data science, Flink, AI and interview experience, side‑hustle earning and career planning.
Veteran data‑warehouse expert explains how the Paimon‑StarRocks stack solves the write‑read split, cuts storage costs, and delivers real‑time analytics, comparing it with Hudi, Iceberg, ClickHouse and Trino, and shows why leading Chinese tech firms are adopting this lakehouse architecture.
This article explores how artificial intelligence enhances data warehouses by automating model design, generating SQL from natural language, optimizing resource scheduling, and enabling business users to converse directly with data, while also reviewing leading tools and future cloud‑native trends.
This article compiles the most frequently asked interview questions for 2026 data‑warehouse development engineers, covering core concepts, layer architecture, SQL optimization, window functions, Hive vs Spark, data skew solutions, modeling metrics, slowly changing dimensions, scheduling tools, data quality monitoring, and real project experience.
The article explains the distinct roles of data warehouses, data marts, and data lakes, illustrates their differences with analogies and real‑world cases, outlines a three‑step strategy for enterprises, highlights common pitfalls, and offers a decision guide to help organizations choose the right architecture for their data needs.
This guide presents a comprehensive big‑data development roadmap, detailing industry opportunities, a six‑module technology stack, four progressive learning stages, hands‑on project ideas, interview question strategies, common pitfalls, and curated resources, helping aspiring engineers become proficient and interview‑ready while avoiding common mistakes.
This article explains Hive's partitioning and bucketing techniques, compares their purposes, advantages, and pitfalls, and shows how to combine them with concrete SQL examples to improve query performance, reduce I/O, and optimize joins and sampling in large data warehouses.
This guide outlines a concise, 1.5‑2‑minute self‑introduction for data engineering interviews, highlighting essential personal details, technical stack, project achievements, business impact, and common pitfalls to avoid, with a concrete example and actionable tips.
This guide walks you through designing and implementing a complete e‑commerce data‑warehouse project—from raw data ingestion and ODS/DWD/DWS/ADS layers to optional real‑time analytics—while highlighting interview‑ready resume tips, common pitfalls, and performance‑tuning tricks.
This article explains the definitions, core components, purposes, and real‑world examples of six architecture layers—business, product, application, data, technical, and project—and shows how they interrelate to form a complete system design framework.
This article analyzes the five typical pitfalls when designing DWS aggregation tables—from chimney‑style schemas to performance blind spots—explains their consequences, and provides concrete, production‑ready recommendations, code examples, and design principles to create reusable, efficient data‑warehouse layers.
