AI-era Data Governance
Put metadata, standards, quality, lineage, permissions, and data assets back into real enterprise engineering scenarios.
BigDataFlowing / Data × AI Engineering
DugufengBigDataFlowing
An enterprise AI engineering writer grounded in data governance practice
For digital leaders, data owners, and technical teams, I write about AI-era data governance, AI for Data, Data for AI, knowledge graphs, ontology, RAG, and enterprise AI Agent engineering. My edge is hands-on validation: running fast-changing technologies myself before judging whether they work in real scenarios.
AI for DataData for AIKnowledge GraphEnterprise AgentMetadataData Governance
10+Years in big data architecture and governance
20K+BigDataFlowing vertical readers
34Returned to postgraduate exam preparation
AIEngineering transformation track
ABOUT
Real experience, not a manufactured persona
The main line is career transition, learning, engineering practice, and continuous writing. Personal history is only the trust layer; the core remains enterprise data and AI engineering problems.
Public Identity
Dugufeng is a long-term practitioner in data governance, big data, and AI engineering, and the author behind BigDataFlowing. He moved from frontline port work into programming, then into big data, data governance, and enterprise data platforms. The long-term habit is hands-on practice, reproduction, troubleshooting, screenshots, and writing, turning new technology into credible and reusable knowledge products or production systems.
01Frontline port work
02Learning programming in Beijing
03Big data engineering
04Enterprise data platforms and governance
05Postgraduate exam at 34
06Deep work in AI engineering
CAPABILITY MAP
Connecting data governance experience to AI engineering
This is not concept chasing or generic AI news. The focus is how enterprises connect data, knowledge, workflow, permissions, and evaluation, then turn validated practice into reusable methods, templates, and systems.
AI for Data
Track how AI changes governance work: metadata enrichment, quality rule generation, lineage explanation, standard recommendation, and governance agents.
Data for AI
Build AI-ready data foundations from the perspectives of RAG, knowledge bases, evaluation data, permission context, and high-quality datasets.
Enterprise AI Agent Engineering
Break down workflow, tools, knowledge bases, evaluation, monitoring, security, and operations required for enterprise Agent adoption.
SELECTED WORK
Selected Work and Project Assets
This page is not a resume. It is a digital business card with deeper paths: knowledge products, case collections, CourseMotion AI, tool maps, and long-term writing.
01
Knowledge System
AI-era Data Governance Practice Library
A structured knowledge product around data governance systems, AI for Data, Data for AI, knowledge graphs, ontology, and enterprise AI Agent engineering.
Data GovernanceAI for DataAgent Engineering
02
Practice Map
Data Governance Case Collection
Translates data middle platforms, realtime computing, metadata, catalogs, lineage, quality, and asset operations into discussable enterprise project scenarios.
MetadataLineageData Assets
03
Production System
CourseMotion AI
A Video-as-Code production system for educational videos, connecting Markdown, knowledge bases, course scripts, HTML scenes, subtitles, and rendering.
Video-as-CodeKnowledge ProductAutomation
04
Tool Atlas
Big Data Tool Navigator
Curates Hadoop, Spark, Flink, Atlas, DataHub, OpenMetadata, and related tools, judging them inside the governance system rather than as isolated links.
Big DataOpen SourceTooling
WRITING / NOTES / CONTENT
BigDataFlowing: turning judgment into durable content
Writing focuses on data governance, AI for Data, Data for AI, knowledge graphs, ontology, enterprise Agent engineering, and policy trends.
Writing and Knowledge Assets
Since 2019, Dugufeng has written technical blogs and public articles on Hadoop, Spark, Flink, Superset, Atlas, DataHub, OpenMetadata, data governance, and metadata management. The current focus is how enterprises build data foundations, knowledge systems, and AI engineering capabilities in the AI era.
Read ArticlesAI-era Data GovernanceKnowledge Graph and OntologyOpenMetadata / DataHub / AtlasRAG and Knowledge GovernanceEnterprise AI Agent EngineeringData Policy and Market Trends
EXPERIMENTS / LAB
Enterprise AI engineering questions under exploration
The lab section holds future projects, prototypes, and research notes. It keeps exploration visible without turning the page into a technical demo reel.
CourseMotion AI
Exploring an engineering loop from Markdown articles to storyboards, HTML scenes, narration, subtitles, and MP4 rendering.
Governance Agent Prototypes
Turning governance reports, quality rules, standard recommendations, and asset inventory into evaluable and auditable enterprise assistance.
Enterprise Semantic Layer
Using ontology, knowledge graphs, and metric definition management to connect business language, data models, and AI context.
AI Engineering Evaluation
Focusing on permissions, context, observability, and continuous improvement for RAG, Agent, and knowledge-base systems.
Tool Map Signal
The site currently curates 73 tools across big data, analytics, governance, processing, and storage, forming reusable material for cases, tutorials, and content systems.
View Governance ToolsLINKS / CONTACT
If you are evaluating data governance, knowledge governance, or enterprise AI Agent adoption
Start with a concrete question: project judgment, solution breakdown, content collaboration, case co-creation, or internal enterprise sharing.
BigDataFlowing / Data keeps flowing into intelligence.