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AI data engineering

7 articles · Page 1 of 1
Lakehouse Research Base
Lakehouse Research Base
Oct 24, 2026 · Big Data

Apache Paimon 2.0 Multimodal Lakehouse: BLOB, VECTOR, and Indexing Realities

This article analyzes Apache Paimon 2.0's multimodal lakehouse capabilities, distinguishing Object Table from BLOB storage, clarifying that VECTOR type alone doesn't enable similarity search, detailing indexing visibility pitfalls, and comparing lakehouse retrieval with dedicated vector databases like Milvus.

AI data engineeringANN searchApache Paimon
0 likes · 14 min read
Apache Paimon 2.0 Multimodal Lakehouse: BLOB, VECTOR, and Indexing Realities
Machine Heart
Machine Heart
Sep 23, 2026 · Industry Insights

Muchen AI: Redefining AI Data Infrastructure Through Verifiable Evaluation Standards

Muchen AI moves beyond data labeling to build long-horizon evaluation systems using structured Rubrics, Docker-based reproducible environments, and automated scoring, raising evaluator consistency from 30% to 90% for code models and extending the framework to scientific research via ScienceBuddy's recursive verification loop across 10+ domains.

AI data engineeringAI infrastructureModel Evaluation
0 likes · 13 min read
Muchen AI: Redefining AI Data Infrastructure Through Verifiable Evaluation Standards
Data Bricklaying Diary
Data Bricklaying Diary
Aug 1, 2026 · Big Data

AI Data Engineering: The Data Supply System for the Agent Era

This article defines AI Data Engineering as a data supply system for large models and agents, extending traditional data engineering with semantic modeling, RAG, controlled data services, permission governance, and feedback loops to make data understandable, retrievable, callable, and auditable for reliable enterprise AI deployment.

AI data engineeringAgentsData Engineering
0 likes · 11 min read
AI Data Engineering: The Data Supply System for the Agent Era
Data Bricklaying Diary
Data Bricklaying Diary
Jul 13, 2026 · Big Data

Building Ontology-Driven Data Governance: Objects, Processes, Roles & Operating Mechanisms

This article presents a comprehensive framework for ontology-driven data governance that extends beyond semantic modeling into a continuous operating system covering three governance object categories, a six-step scenario-based process, eight cross-functional roles, seven operating mechanisms, and integration with existing data governance capabilities to support AI-ready semantic assets and Agent actionability.

AI data engineeringAgent SystemsOntology-Driven Data Governance
0 likes · 16 min read
Building Ontology-Driven Data Governance: Objects, Processes, Roles & Operating Mechanisms
Data Bricklaying Diary
Data Bricklaying Diary
Jul 10, 2026 · Big Data

High-Quality Datasets: The New Data Governance Battlefield After DCMM 2.0

The article argues that post-DCMM 2.0, data governance must evolve from asset management to building high-quality datasets—trustworthy, semantically clear, quality-measurable, version-traceable, and compliant—to reliably support AI training, evaluation, knowledge augmentation, and agent workflows, requiring semantic foundations and AI data engineering.

AI data engineeringDCMM 2.0Data Governance
0 likes · 12 min read
High-Quality Datasets: The New Data Governance Battlefield After DCMM 2.0
AI Waka
AI Waka
May 22, 2026 · Artificial Intelligence

How Skills Can Cut Costs and Speed Up High‑Quality LLM Data Pipelines

The article explains how the open‑source DataFlow‑Skills framework lets LLM agents plan, validate, and execute data cleaning and synthesis pipelines with strict field contracts and specialized operators, dramatically reducing costly failures and accelerating high‑quality training data production.

AI data engineeringDataFlowLLM data pipelines
0 likes · 15 min read
How Skills Can Cut Costs and Speed Up High‑Quality LLM Data Pipelines