Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
This article examines Datus AI’s open‑source Data Engineering Agent, detailing its architecture, interactive context engineering, evaluation results, and future roadmap, and explains how it tackles the challenges of scaling AI‑driven data workflows.
This article analyzes the architectural evolution of Alibaba Cloud Hologres from a fragmented multi‑engine data stack to the All‑in‑One design of Hologres 4.0, detailing its multimodal search, AI‑native functions, performance benchmarks, resource governance, lake integration, and real‑world deployment scenarios.
This article analyzes why Retrieval‑Augmented Generation (RAG) often underperforms in enterprise production, identifies eight common pitfalls—from document parsing to token costs—and offers a systematic roadmap of diagnostics, hybrid search, reranking, and deployment strategies presented by leading AI experts.
The whitepaper explains how the explosive growth, multimodal nature, and real‑time collaboration demands of intelligent connected‑vehicle data create two “impossible triangles,” and how SelectDB’s three technical innovations—Index+Bitmap primary keys, Variant sparse columns, and hybrid full‑text/vector search—enable cost‑effective, high‑performance real‑time analytics across five automotive scenarios with proven case studies from leading OEMs.
This article reviews the rapid evolution of code intelligence, covering the history of code foundation models, reinforcement‑learning optimizations, scaling‑law insights, the LoopCoder architecture, rigorous multi‑level evaluation suites, and the emergence of repository‑level code agents, while highlighting open‑source contributions such as Qwen‑Coder.
This article explores the evolution of e‑commerce search toward multimodal and cross‑modal capabilities, outlines a generic architecture that combines text and image processing via embedding and vector retrieval, and demonstrates how to implement the solution using Alibaba Cloud's AI Search Open Platform and Elasticsearch Serverless with detailed guidance on models, similarity metrics, quantization, and performance optimization.
The article examines costly data platform failures—such as a $40 million payroll system collapse and a healthcare.gov outage—highlighting why traditional data middle platforms become data swamps, then explains how Palantir’s ontology approach, with its three‑layer semantic, dynamics, and decision architecture, can turn data into actionable insights and achieve triple‑digit ROI.
The article analyzes costly data‑platform failures—such as a $40 million school‑district payroll system and a collapsed Healthcare.gov launch—identifies the root cause as ineffective data middle platforms, and explains how Palantir’s ontology‑based three‑layer architecture (semantic, dynamics, decision) transforms raw data into automated business actions, delivering measurable ROI across multiple industries.
In the era of AI and multi‑cloud, this article analyzes the core challenges of data governance—data silos, quality gaps, and compliance risks—and explains how Apache Gravitino’s unified metadata architecture together with OpenLineage’s standardized lineage model provide a scalable, automated solution for intelligent, real‑time data management.
This article presents a comprehensive, end‑to‑end solution for multimodal product search, detailing the evolution from keyword to image‑based queries, the core embedding and vector retrieval technologies, practical Elasticsearch Serverless integration, quantization methods, and a complete demo workflow for building a high‑performance, low‑cost search platform.
