Agent skill · data analytics · aradotso
retail-etl-medallion-pipeline
End-to-end retail ETL pipeline using Medallion Architecture (Bronze/Silver/Gold) with TSQL, PySpark, and Airflow for inventory, sales, and supplier data processing
Why this skill is useful
Provides specific TSQL and PySpark scripts for implementing a retail ETL pipeline using Medallion Architecture that the AI wouldn't generate on its own.
What it needs
Requires docker installed locally. About 8k tokens when loaded. Last updated 2026-08-05. 5 stars on the source repository.
What this skill does
Retail ETL Medallion Pipeline Skill Skill by ara.so — Data Skills collection. Overview This project implements a production-grade Medallion Architecture ETL pipeline for retail/hypermarket data, handling complex business logic like inventory shrinkage, meat/poultry recipe conversions, supplier rebate tiers, and multi-branch sales consolidation. The architecture follows three data quality layers: Bronze Layer: Raw data ingestion from CSV sources (sales, stock, products) Silver Layer: Cleaned, standardized, and business-rule-applied data Gold Layer: Aggregated, analytics-ready dimensional models The pipeline processes: Multi-branch sales transactions (Alex, Cairo, Giza) Product catalogs with recipe/yield conversions Stock/inventory tracking across locations Supplier rebate calculations Project Structure Installation & Setup 1. Infrastructure Setup (SQL Server) Using Docker: Or use an existing SQL Server instance (2017+). 2. Database Initialization This creates: Database: RetailDataWarehouse Schemas: bronze, silver, gold, staging 3. Load Raw Data to Bronze Layer Place CSV files in accessible location, then run: Or execute all Bronze scripts sequentially: Key Architecture Patterns Bronze Layer (Raw Ingestion) Purpose: Land raw data with minimal transformation. Add audit columns only. Silver Layer (Cleaned & Standardized) Purpose: Apply data quality rules, deduplication, and business transformations. Key Silver Transformations: Date standardization Recipe yield conversions for perishables Duplicate removal Null handling and imputation Data quality scoring Gold Layer (Analytics-Ready Aggregates) Purpose: Create dimensional models and pre-aggregated metrics for BI tools. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/retail-etl-medallion-pipeline