Sharing cutting‑edge big data and AI technologies, with occasional lifestyle insights.
DeepSeek-OCR 2 introduces a causal‑flow encoder‑decoder architecture that replaces fixed‑grid scanning with semantic token reordering, achieving higher accuracy and lower token budgets on diverse document benchmarks while being fully open‑source on GitHub and Hugging Face.
Alibaba's Tongyi team released the Qwen3‑Max‑Thinking model, surpassing one trillion parameters and 36 T tokens of pre‑training, introducing adaptive tool‑calling and test‑time expansion techniques that boost benchmark scores across 19 tests, positioning it alongside top international models and now available via app, web, and API.
The article proposes a “digital portrait” framework for data governance, outlining concrete metrics to evaluate user experience across external customers, internal users, management, and technical staff, as well as architecture quality indicators such as model reuse, data distribution, standard stability, and asset coverage.
The article details how eBay's Pronto platform implements an adaptive capacity management system for hundreds of Elasticsearch clusters, describing the challenges of uniform resource allocation, traffic spikes, and resource waste, and outlining a multi‑step process that includes monitoring, pre‑check, analysis, throttling, scaling, and post‑check to ensure high availability and efficient resource use.
The article provides a detailed technical analysis of Apache Atlas, describing its core components, type system, JanusGraph‑based graph storage on HBase, the mapping of various metadata types to vertices and edges, and the three‑phase parsing and validation process that together support scalable Hadoop governance.
This article introduces fifteen commonly used data analysis models for market work, detailing each model's purpose, core components, advantages, disadvantages, and practical application in market analysis, research, positioning, and marketing.
The article outlines the essential principles and step‑by‑step design of a three‑layer data warehouse—source (ODS), historical, and data‑model layers—covering data consistency, universality, storage strategies, usability, and security to help architects construct reliable analytical platforms.
The article explains that count(1) and count(*) produce identical results and similar execution plans, while count(column) skips NULL values; it also compares their performance under various table schemas and provides a MySQL example demonstrating these differences.
The article explains Elasticsearch's cluster architecture, covering nodes, shards, replicas, two deployment models, data‑layer design, and the trade‑offs of each approach, while also comparing alternative distributed storage architectures and their impact on reliability and performance.
This article walks through the DWD (Data Warehouse Detail) layer design, explaining its purpose, the four-step modeling process, and detailed implementations of transactional, periodic snapshot, and cumulative snapshot fact tables with Hive SQL examples and schema definitions.
