Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
The article analyzes how Google Cloud’s BigQuery Graph with Measures expands the traditional semantic layer—moving from unified metric definitions to a governed business‑relationship model that enables agents to answer not only what happened but also why it happened.
Palantir's new Agent Stack shifts AI agents from short‑lived model‑prompt loops to a production‑grade architecture that adds state, events, effects, durable execution, observability and ontology, turning agents into reliable, governable software components for real‑world business tasks.
AWS’s new Context Ontology Accelerator transforms the traditional semantic layer from merely calculating metrics into a runtime business‑context infrastructure that agents can query, combining governed metrics, ontology, and knowledge‑graph services via MCP to enable accurate, auditable, and governed AI‑driven decisions.
The article examines how Graphwise’s recent acquisition by Oakley Capital signals a shift of the semantic layer from a BI‑focused metric unifier to a core enterprise AI infrastructure that powers AI agents with business‑level knowledge graphs, detailing the technology stack, market traction, and strategic implications.
The article analyzes how OpenAI, Anthropic (Claude), Google, and DeepSeek are converging on a shared "harness" layer that separates model capabilities from execution, detailing each company's implementation, the trade‑offs of complexity, and the emerging competition focused on model‑harness co‑optimization.
The article analyzes how Google Cloud’s new BigQuery Graph with Measures expands the traditional semantic layer—adding explicit business entities and relationships—to enable agents to trace why metrics change, illustrated with retail case studies, Graph‑Measure integration, and Knowledge Catalog enhancements.
The article analyzes how AI applications evolve from answering questions to executing tasks, outlines the data, context, and execution challenges, and explains how Elasticsearch Agent Builder integrates searchable data, tool capabilities, and governance into a verifiable execution chain, illustrated with a log‑analysis case study.
The article analyzes Oakley Capital’s acquisition of a majority stake in Graphwise, explains how the company’s semantic layer technology is evolving from unified business metrics to a foundational AI agent infrastructure, and outlines the technical components and market implications of this shift.
The article explains how Google’s new BigQuery Graph with Measures extends the traditional semantic layer—once focused on unified metric definitions—into a business‑context‑aware infrastructure that lets agents trace why metrics change through explicit entity relationships.
The article analyzes cutting‑edge agentic RAG designs, LLM‑enhanced recommendation pipelines, and generative ranking models from Alibaba Cloud, Huawei Noah, and Baidu, detailing their architectures, multi‑modal retrieval strategies, GPU acceleration, and measured performance gains.
