Egyptian Cosmetics Analytics Engineering Platform
An Egypt-focused retail portfolio project connecting operational data to a dimensional warehouse and Power BI reporting.
A question. A process.
Work you can explore.
Follow the decisions behind the implementation, then inspect the original work.
Chapter 1 of 4 · The question
The question behind the build.
An omnichannel retail scenario brings together orders, customer records, inventory, targets, and exchange-rate inputs. Mixed formats and inconsistent values need a clear treatment before they can support reporting.
Follow the implementation.
- 01
Ingest CSV, Excel, and JSON inputs with Python and preserve raw records with lineage.
- 02
Standardize fields, validate relationships, and route invalid records into explicit quarantine tables.
- 03
Model dimensions and facts in SQL Server, including customer history with SCD Type 2.
- 04
Provide SQL Server and Power Query delivery paths with a DAX semantic layer.
See the work. Inspect the details.
The useful part is the complete chain: a source record can be traced through cleaning, validation, modeling, and reporting. The repository documents the scenario, architecture, and data-quality decisions.
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The source
keeps moving.
Source updated
Enterprise Multi-Source ELT, SQL Server 2022 Kimball Data Warehouse, SCD Type 2, Automated Data Quality Quarantine & Power BI Analytics Lifecycle
Current README excerpt
[](https://www.python.org/) [](https://www.microsoft.com/en-us/sql-server/) [](https://powerbi.microsoft.com/) [](https://learn.microsoft.com/en-us/powerquery-m/) [](https://learn.microsoft.com/en-us/dax/) [](#-data-quality-framework--quarantine-ledger) [](https://github.com/Sohila-Khaled-Abbas/egyptian-cosmetics-market/actions/workflows/cidatavalidation.yml) [](#-kimball-star-schema-data-warehouse) [](#-business-scenario) [](LICENSE)
A source excerpt, not an independently verified outcome.