Practical insights from the GuanYuan Data Tech Team
Guandata’s R&D leader outlines how their analytics platform leverages Delta Lake and Spark to deliver fast, ACID‑compliant BI and AI workloads, detailing architecture, key features like schema evolution and time travel, and practical performance tricks such as compaction, vacuuming, and multi‑engine integration.
This article explains why and how to evaluate machine learning model quality through a structured framework that covers data validation, feature checks, and algorithm testing, helping ensure accuracy, reliability, and maintainability before deployment.
This article traces the evolution of the modern data stack, explaining the shortcomings of traditional data warehouses, the rise of cloud‑native ELT, self‑service analytics, analytics‑as‑software, and enhanced decision intelligence, while highlighting emerging trends and practical implementations.
This article explains how beauty brands can increase repeat purchase rates by building high‑potential member prediction models, applying tiered segmentation, and leveraging various AI‑powered models—including natural repurchase, purchase power, marketing response, and uplift models—to optimize targeting, ROI, and overall sales performance.
This article explains how AI‑driven forecasting, joint error‑distribution safety‑stock calculations, and MLOps‑backed simulation are combined to optimize DTC replenishment, improve inventory days and stock‑out rates, and address practical deployment challenges in fast‑growing e‑commerce supply chains.
This article explains the concept, significance, and practical case studies of MLOps—showing how integrating DevOps principles with data and machine learning creates reliable, automated pipelines for data quality, model monitoring, error analysis, and continuous integration, ultimately accelerating AI delivery.
This article explains why a data‑analysis platform needs a plugin architecture for custom charts, compares iframe‑based and Web Components approaches, shows how to implement a lightweight ECharts‑based chart plugin, and provides step‑by‑step code samples and technical selection criteria for frontend developers.
This article examines the technical challenges of intelligent replenishment—model stability, complexity, generalization, and interpretability—and explains how a few‑shot imitation learning and inverse reinforcement learning framework can overcome these issues to deliver reliable, low‑cost AI‑driven supply‑chain decisions.
The article investigates a Spark driver CPU overload caused by the compatiblePartitions method’s expensive permutation logic in window functions, explains the underlying O(n!) complexity, and presents a simplified implementation that eliminates the issue and has been merged into the official Spark codebase.
This article explores the evolution and architecture of cloud‑native data platforms, covering their historical roots, modern components such as storage layers, ingestion, processing, metadata, and consumption, and offers practical guidance on selecting tools, designing pipelines, and implementing best‑practice strategies for scalable, flexible data infrastructure.
