Hello Dev Community! 👋
It is officially Day 81 of my 100-day full-stack engineering run! Yesterday, I locked down linear logical operators to sweep across singular tabular records. Today, I stepped into the world of database analytics and reporting architectures by mastering: The GROUP BY Clause, the HAVING Clause, and Categorical Data Aggregations! 📊📈
When processing e-commerce telemetry (like counting orders per category) or handling payment services (like tracking successful transactions per payment channel), you cannot just inspect flat datasets. You must condense rows into meaningful summaries. Today, I built exactly that.
As visible in my workspace metrics inside "Screenshot (181).png", I configured a sample transaction model named banking to parse multi-variable row groupings through a three-tier optimization layer:
1. Categorical Segments via GROUP BY
