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AI data preparation

4 articles · Page 1 of 1
Data Integration and Governance
Data Integration and Governance
Oct 15, 2026 · Artificial Intelligence

Before Enterprise LLM Adoption: Master These 3 Data Types for Accurate AI

This article explains why enterprises must govern master data, metadata, and reference data before integrating large language models, detailing how each type ensures accurate object identification, clear metric definitions, and consistent classification standards for reliable AI-driven analysis.

AI data preparationFineDataLinkLarge Language Models
0 likes · 18 min read
Before Enterprise LLM Adoption: Master These 3 Data Types for Accurate AI
Data Integration and Governance
Data Integration and Governance
Oct 13, 2026 · Big Data

What Makes a Dataset 'Good' for AI? Six Quality Dimensions & the Engineering Pipeline

The article defines high-quality datasets in the AI era by six dimensions—accuracy, completeness, consistency, timeliness, understandability, and traceability—explains why AI amplifies data quality requirements, and outlines a five-step engineering pipeline (aggregation, cleaning, unification, validation, traceability) to transform raw data into reliable AI-ready datasets, emphasizing that continuous data engineering, not data hoarding, enables AI success.

AI data preparationData EngineeringFineDataLink
0 likes · 14 min read
What Makes a Dataset 'Good' for AI? Six Quality Dimensions & the Engineering Pipeline
Data Bricklaying Diary
Data Bricklaying Diary
Jul 20, 2026 · Big Data

High-Quality Datasets: Beyond Cleaned Data for AI Tasks

This article defines high-quality datasets as task-oriented data products with business semantics, evidence traceability, usage boundaries, and continuous governance — not merely cleaned data — and explains why they are essential for reliable AI training, RAG, and Agent applications.

AI data preparationAgentRAG
0 likes · 13 min read
High-Quality Datasets: Beyond Cleaned Data for AI Tasks
Lakehouse Research Base
Lakehouse Research Base
Jan 29, 2026 · Big Data

Unstructured Data Lake Ingestion: File Body + Metadata Registration Patterns

This article details the industry-standard dual pattern of file body storage and metadata registration for unstructured data lake ingestion, covering a three-layer architecture, batch and real-time implementation steps using Paimon and Flink, AI-specific optimizations like label standardization and format conversion, and key operational considerations for permissions, versioning, and cost control.

AI data preparationData LakeFlink
0 likes · 11 min read
Unstructured Data Lake Ingestion: File Body + Metadata Registration Patterns