Ndumiso-Manyathi/Integrated_Project_Maji_Ndogo

Diving into Maji Ndogo's expansive database containing 60 000 records spread across various tables. Leveraging data-driven solutions, it aims to understand water access issues, assess water quality, and propose strategies for improvement. Then leveraging data-driven solutions, aiming to understand water access issues, assess water quality. # Integrated_Project_Maji_Ndogo Exploring **Maji Ndogo’s database** (60,000+ records) to produce data-driven insights on water access, quality, and pollution using SQL. This repository demonstrates workflows from fundamental queries to advanced analytics and prepares actionable outputs aligned with the **UN Sustainable Development Goals (SDGs)**. --- ## 🌍 A Journey Through Maji Ndogo’s Data Maji Ndogo is a fictional community, but its challenges echo those faced in many real regions where water is scarce, polluted or unevenly distributed. At the heart of this project lies a database of more than **60,000 records**, spread across multiple tables, each one carrying pieces of the puzzle: water sources, field visits, pollution records and the people responsible for ensuring access to safe water. This repository is not just about writing SQL queries; it is about telling a story through data. From our first steps exploring the schema to the final stage of preparing insights for decision-makers, every query uncovers a new chapter in the unfolding water crisis of Maji Ndogo. ## 📊 Database Schema Key tables: - `employee` — field staff and surveyors - `location` — towns, provinces, regions - `water_source` — wells, taps and source metadata - `water_quality` — chemical/biological quality assessments - `well_pollution` — contamination incidents and measurements - `visits` — site inspections and survey entries - `global_water_access` — international comparison metrics --- ## 📖 The Story Unfolds We begin by opening the database for the first time. Each is a fragment of the bigger picture. At first, we simply learn the landscape: running **basic SELECT queries**, filtering rows and cleaning anomalies. These early queries are the prologue, teaching us how the data is structured and where its weak points lie. As we progress, we zoom into the crisis itself. Using **aggregations and window functions**, we identify which provinces struggle most, which towns rely on unsa …