Author

Data Practical Notes

Focuses on data visualization, providing practical data visualization insights, introductions to data visualization tools, and the application status of data visualization across industries.

Articles

Likes

Views

Comments

Latest from Data Practical Notes

46 recent articles

Data Practical Notes

Nov 3, 2026 · Big Data

Why Your Data Doubles: 5 Causes & Proven Deduplication Strategies

This article analyzes five root causes of data duplication, classifies duplicate types, compares SQL deduplication methods (DISTINCT, GROUP BY, ROW_NUMBER), covers source-level prevention with unique constraints and idempotency, explains real-time CDC deduplication strategies, and outlines a five-step process for building sustainable data quality governance.

CDCETLSQL

0 likes · 18 min read

Why Your Data Doubles: 5 Causes & Proven Deduplication Strategies

Data Practical Notes

Nov 2, 2026 · Big Data

Exactly-Once Semantics: Why 'Processed Once' Isn't That Simple in Data Pipelines

This article explains exactly-once processing semantics in data engineering, contrasting it with at-most-once and at-least-once, detailing why retries cause duplicates, the roles of checkpoints, idempotency, and transactions, why technical exactly-once doesn't guarantee business correctness, and the four critical failure tests needed before production deployment.

checkpointdata pipelinedata-consistency

0 likes · 16 min read

Exactly-Once Semantics: Why 'Processed Once' Isn't That Simple in Data Pipelines

Data Practical Notes

Oct 30, 2026 · Big Data

Why Data Warehouses Aren't Enough: The Essential Role of Metric Platforms in Unified Analytics

This article explains the distinct roles of data warehouses, metric platforms, and BI tools, demonstrating why a data warehouse alone cannot unify business metric definitions, and provides a five-step framework for building a metric management system that ensures consistent calculations, version control, and cross-departmental reuse.

BIbusiness metricsdata analytics

0 likes · 17 min read

Why Data Warehouses Aren't Enough: The Essential Role of Metric Platforms in Unified Analytics

Data Practical Notes

Oct 29, 2026 · Big Data

Data Sync Simplified: 100+ Sources, CDC, Batch & Incremental in One Platform

This article explores the complexities of data synchronization across heterogeneous systems and introduces FineDataLink 5.0 as a unified platform supporting 100+ data sources with offline batch, incremental, and CDC real-time sync modes, visual ETL, data quality checks, and task scheduling for reliable data pipelines.

CDCETLFineDataLink

0 likes · 16 min read

Data Sync Simplified: 100+ Sources, CDC, Batch & Incremental in One Platform

Data Practical Notes

Oct 28, 2026 · Big Data

Data Lineage Implementation: Table, Field, and Metric Levels Explained

This article explains how to implement data lineage at three levels—table, field, and metric—detailing the purpose, methods, and limitations of each, and recommends a phased approach for enterprises to build traceable, assessable, and maintainable data governance.

ETLSQL parsingdata governance

0 likes · 19 min read

Data Lineage Implementation: Table, Field, and Metric Levels Explained

Data Practical Notes

Oct 27, 2026 · Big Data

Real-Time vs Batch Processing: Why Enterprises Need Both Data Pipelines

This article contrasts real-time and offline computing, showing how they serve distinct business needs—real-time for immediate alerts, batch for accurate periodic analysis—and provides a practical framework for choosing and combining both while keeping key metrics consistent.

FineDataLinkbatch processingbig data

0 likes · 13 min read

Real-Time vs Batch Processing: Why Enterprises Need Both Data Pipelines

Data Practical Notes

Oct 16, 2026 · Big Data

Data Lake vs. Warehouse vs. Lakehouse: Differences, Pitfalls, and Pipeline Essentials

This article clarifies the distinctions between data lakes, data warehouses, and lakehouse architectures, explaining their core philosophies, ideal use cases, common pitfalls like data swamps, and why stable data pipelines matter more than architectural buzzwords.

data architecturedata governancedata integration

0 likes · 17 min read

Data Lake vs. Warehouse vs. Lakehouse: Differences, Pitfalls, and Pipeline Essentials

Data Practical Notes

Oct 13, 2026 · Artificial Intelligence

Jev: The 'System One' Model for Structured Judgments That LLMs Can't Reliably Provide

Jev is a 'System One' model that returns structured judgments with probabilities instead of free text, enabling reliable integration into software workflows for high-frequency classification tasks where LLMs' unstructured output creates parsing overhead and instability.

AI architectureJSON alternativeJev

0 likes · 18 min read

Jev: The 'System One' Model for Structured Judgments That LLMs Can't Reliably Provide

Data Practical Notes

Oct 24, 2025 · R&D Management

Low-Code Development: Benefits, Limitations, and Best Use Cases

This article explains low-code development, its core benefits like faster delivery and lower technical barriers, key limitations including vendor lock-in and performance ceilings, and identifies ideal use cases such as internal enterprise apps, workflow automation, and rapid prototyping.

citizen developmententerprise applicationslow-code

0 likes · 11 min read

Low-Code Development: Benefits, Limitations, and Best Use Cases

Data Practical Notes

Oct 22, 2025 · Industry Insights

Informatization vs. Digitalization: What's the Real Difference?

This article distinguishes informatization from digitalization by comparing their core focus, goals, thinking patterns, technologies, value creation, and evolutionary relationship, using examples like expense reimbursement and procurement to illustrate how digitalization leverages data to transform business models beyond mere process efficiency.

DigitalizationERPInformatization

0 likes · 11 min read

Informatization vs. Digitalization: What's the Real Difference?