Data Engineering 14
- Data Modeling in Modern Data Architecture: From Layers to Lakehouse
- From Prep Kitchens to Open Kitchens: An ETL vs. ELT Story
- Data Warehouse vs Data Lake vs Lakehouse: Explained Simply
- The Midnight Symphony: When Schema Drift Breaks
- DevLog #3 - Building Products Alone: AI-Assisted Development
- Zero-Tolerance Schema Drift: The CI/CD Truth
- Rule-Driven Schema Validation: A Lightweight Solution
- DevLog#2: Why I Scrapped My Half-Built Data Validation Platform
- SQL Pushdown vs Spark: Smarter Data Validation, Lower Cost
- DevLog #1 - ValidateLite: Building a Zero-Config Data Validation Tool
- Missing Orders or Calculation Errors? Data Integrity Validation Reveals the Truth
- When Production Data Explodes: 10-Minute Time Travel with Snapshot Tables
- The Last ETL You'll Ever Trust: Why Diff Check is Data Engineering's Silent Guardian
- The Five Pillars of Data Truth: How Quality Metrics Solve Report Mismatches