retail-etl-pipeline-medallion

To build a retail ETL pipeline using Medallion Architecture, you automate data ingestion, transformation, and aggregation across Bronze, Silver, and Gold layers. This Skill provides SQL scripts and PySpark logic to orchestrate retail data warehousing workflows end-to-end.

Medallion Architecture in data warehousing organizes data into Bronze, Silver, and Gold layers to progressively improve data quality and structure. You need it when automating retail ETL processes to ensure clean, aggregated data for analytics across various sales and inventory sources.

Yes, you need PySpark and Apache Airflow to automate retail data transformation with this pipeline. PySpark handles the data processing across the Medallion Architecture layers, while Apache Airflow orchestrates the ETL workflows, and SQL Server serves as the data warehouse backend.

Yes, you can use SQL Server for retail sales and inventory data warehousing with this ETL pipeline. The pipeline integrates data from various retail sources into SQL Server, applying transformations across Bronze, Silver, and Gold layers to ensure data quality and enable analysis.

The best way to structure retail data ingestion and aggregation is using a Medallion Architecture approach. This pipeline automates the ETL process by moving raw retail data through Bronze, Silver, and Gold layers using PySpark, ultimately aggregating it in SQL Server for robust analysis.

Separating Bronze, Silver, and Gold layers in your retail data transformation pipeline ensures structured data quality improvement. Bronze holds raw ingested data, Silver applies transformations, and Gold delivers aggregated, analytics-ready retail data, simplifying the overall data warehousing process.