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Data Integration and Governance
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Data Integration and Governance

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Latest from Data Integration and Governance

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Data Integration and Governance
Data Integration and Governance
Sep 4, 2026 · Big Data

ETL vs ELT vs ETLT: Where Should Data Transformation Live?

This article explains the core differences between ETL, ELT, and ETLT data integration patterns, emphasizing that the key distinction is where transformation occurs—before or after loading—and provides five decision criteria for choosing the right approach based on data latency, raw data retention, compute costs, and business logic volatility.

Data PipelineELTETL
0 likes · 16 min read
ETL vs ELT vs ETLT: Where Should Data Transformation Live?
Data Integration and Governance
Data Integration and Governance
Sep 1, 2026 · Big Data

Building a Real-Time Data Warehouse: End-to-End Pipeline from Kafka to BI

This article details the end-to-end architecture of a real-time data warehouse, covering data ingestion via CDC and Kafka, stream processing challenges like deduplication and event-time handling, layered modeling (ODS/DWD/DWS/ADS), unified metric definitions, and critical production monitoring for latency, lag, data reconciliation, and consistency with offline results.

CDCData MonitoringFlink
0 likes · 20 min read
Building a Real-Time Data Warehouse: End-to-End Pipeline from Kafka to BI
Data Integration and Governance
Data Integration and Governance
Aug 31, 2026 · Big Data

Data Agent Architecture: 7 Layers to Connect Enterprise Data Beyond Text-to-SQL

This article argues that enterprise Data Agents require a seven-layer data architecture—source, preparation, object, semantic, permission, execution, and verification—rather than simply connecting databases to LLMs, detailing how each layer resolves ambiguity, ensures stability, enforces security, and enables trustworthy analytical reasoning.

AI AnalyticsBusiness IntelligenceData Agent
0 likes · 18 min read
Data Agent Architecture: 7 Layers to Connect Enterprise Data Beyond Text-to-SQL
Data Integration and Governance
Data Integration and Governance
Aug 28, 2026 · Big Data

What Does Metric Management Actually Manage? The Full Lifecycle Explained

The article explains that metric management is not just cataloging metrics but governing their full lifecycle—business definitions, calculation rules, responsibility assignment, data lineage, and versioned change control—to ensure metrics are trustworthy, traceable, and sustainably governed across business, data, and technical layers.

Data GovernanceFineDataLinkcalculation rules
0 likes · 19 min read
What Does Metric Management Actually Manage? The Full Lifecycle Explained
Data Integration and Governance
Data Integration and Governance
Aug 21, 2026 · Fundamentals

How to Manage Data Quality? A Complete Breakdown of the Six Dimensions

Enterprises often start data governance with standards, yet the real challenge is answering “Is this data accurate?” – a challenge solved by a continuous mechanism that discovers, locates, resolves, and verifies issues across six dimensions (completeness, consistency, accuracy, uniqueness, timeliness, validity) and follows a five‑step governance loop.

Data Governanceaccuracycompleteness
0 likes · 16 min read
How to Manage Data Quality? A Complete Breakdown of the Six Dimensions
Data Integration and Governance
Data Integration and Governance
Aug 18, 2026 · Industry Insights

How to Achieve Real‑Time PLC Data Collection: End‑to‑End Device Integration and Platform Delivery

The article walks through the full PLC real‑time data pipeline—from defining device data boundaries and handling heterogeneous field protocols with edge gateways, to designing sampling and reporting cycles, standardizing data layers, and converting raw signals into business events for reliable long‑term analytics.

FineDataLinkPLCReal-time Data
0 likes · 15 min read
How to Achieve Real‑Time PLC Data Collection: End‑to‑End Device Integration and Platform Delivery
Data Integration and Governance
Data Integration and Governance
Aug 17, 2026 · Artificial Intelligence

How Data Agents Access Enterprise Data: 4 Solution Paths (DB, API, Warehouse, Knowledge Base)

The article breaks down four ways a Data Agent can reach enterprise data—direct database connections, business APIs, data‑warehouse layers, and knowledge‑base/RAG—detailing their strengths, limitations, and how combining them with stable integration pipelines enables reliable AI‑driven analytics and actions.

API IntegrationData AgentDatabase Access
0 likes · 20 min read
How Data Agents Access Enterprise Data: 4 Solution Paths (DB, API, Warehouse, Knowledge Base)
Data Integration and Governance
Data Integration and Governance
Aug 13, 2026 · Artificial Intelligence

Why Traditional Wide-Table Data Warehousing Won’t Suffice in the Data Agent Era

The article argues that while wide tables remain useful for fixed, high‑frequency analyses, the rise of Data Agents requires data warehouses to go beyond simple tables and provide business semantics, unified metrics, context, and governance so AI can understand and answer complex business questions accurately.

AI AnalyticsData AgentData Governance
0 likes · 18 min read
Why Traditional Wide-Table Data Warehousing Won’t Suffice in the Data Agent Era