Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
Palantir achieves 85% revenue growth by building a business ontology layer that integrates enterprise data semantics, enabling reliable AI decisions in high-stakes environments, unlike fragile RAG or wrapper approaches that fail when models commoditize.
This article reviews a 2026 paper on Graph Engineering for LLM agents, detailing the shift from individual agent intelligence to system intelligence via explicit task DAGs, runtime state management with checkpoints and replay, multi-agent coordination through capability modeling, and ontology engineering for shared semantics.
Databricks Genie Ontology combines governed Unity Catalog semantics with automatically inferred context from SQL queries, dashboards, and agent interactions, using authority scoring to resolve conflicting business definitions so AI agents know which Revenue, Active User, or Qualified Lead definition the organization currently trusts.
Google's BigQuery Graph with Measures integrates governed metrics with property graphs to give AI agents both accurate calculations and traversable business relationships, enabling root-cause analysis beyond simple metric queries, with zero-ETL mapping from existing tables and Looker integration.
Knora 4.2 introduces an AI-driven Forward Deployment Engineering (AI-FDE) loop that automates ontology construction, knowledge extraction, skill building, and agent execution, demonstrating 87.5% faster defect investigation and 72% less repetitive analysis across five enterprise scenarios including production quality, operations tracing, and cost management.
Palantir CEO Alex Karp argues that AI-era competition splits enterprises into those with domain-specific AI-enhanced infrastructure and those without, warning that generic AI adoption creates no competitive advantage while specialization of proprietary knowledge builds unbeatable moats.
This article details an ontology-driven three-layer architecture for enterprise office agents, replacing standard RAG with GraphRAG to achieve verifiable, auditable AI. It covers a six-step modeling method, a six-dimensional evaluation system, a two-week MVO rollout, and three production scenarios — document review, meeting minutes, and document structuring — showing how semantic assets become the true competitive moat.
Tencent Cloud's big data platform introduces computing intelligence as a unified paradigm for structured SQL and multimodal AI workloads, detailing engines Meson, Xpark, TCRay, and ES that enable GPU-accelerated SQL, distributed multimodal processing, unified CPU/GPU scheduling, and evaluatable RAG for agent-ready data platforms.
The article details Alibaba Cloud's AI‑driven search upgrades, covering data growth, multi‑tenant elasticity, five new engine capabilities, performance benchmarks, and the shift toward a context‑infrastructure that integrates agents, memory, and knowledge engines.
MotherDuck’s experiment shows that while AI agents can automatically build a Malloy semantic layer, the layer does not improve token efficiency or answer accuracy compared with plain Markdown + SQL, and the real long‑term asset may be the evaluative tests rather than the semantic model itself.
