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high-quality datasets

5 articles · Page 1 of 1
Lakehouse Research Base
Lakehouse Research Base
Aug 19, 2026 · Big Data

How 1565PB of High-Quality Datasets Are Reshaping Lakehouse Architecture for AI

China's high-quality dataset count hit 120,000 totaling 1,565 PB with 60% quarterly growth, exposing four systemic gaps in traditional BI-oriented lakehouse architectures — storage, governance, compute performance, and security — and driving an AI-native reference architecture built on Paimon, StarRocks, tiered storage, operator-level lineage, and multi-modal retrieval.

AI training dataApache PaimonStarRocks
0 likes · 43 min read
How 1565PB of High-Quality Datasets Are Reshaping Lakehouse Architecture for AI
Frontline Investigation
Frontline Investigation
Jul 19, 2026 · Artificial Intelligence

High-Quality AI Datasets: Why Business Judgment Beats Data Volume

The article explains that high-quality AI datasets for industry require scenario samples capturing complete business judgment cycles, expert involvement in defining evaluation samples, and continuous feedback from production, not merely accumulating more raw data.

Chinese data policyIndustry AIbusiness judgment
0 likes · 12 min read
High-Quality AI Datasets: Why Business Judgment Beats Data Volume
Data Bricklaying Diary
Data Bricklaying Diary
Jul 11, 2026 · Industry Insights

Why AI-Era Data Governance Requires Ontology-Driven High-Quality Datasets

This article traces data governance evolution from master data consistency and metadata visibility to ontology-driven business semantic modeling, arguing that AI-era governance must produce high-quality datasets that are AI-usable, trustworthy, evaluable, and continuously optimized through explicit business object, process, rule, and evidence modeling.

AI data supplyDCMM 2.0business semantic modeling
0 likes · 14 min read
Why AI-Era Data Governance Requires Ontology-Driven High-Quality Datasets
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 compliance
0 likes · 12 min read
High-Quality Datasets: The New Data Governance Battlefield After DCMM 2.0