Power BI + SQL sales project released
Ruth Chika published a real‑world sales analysis project that builds interactive dashboards from database SQL to Power BI—project code and implementation details are available on GitHub for replication. (x.com)
The GitHub repository for the sales project is an end‑to‑end build that extracts and cleans AdventureWorksDW2019 tables and delivers interactive Power BI dashboards for internet sales overview, customer detail pages, and product performance views. (github.com) The repo includes explicit SQL scripts named DIM_Customers.sql, DIM_Products.sql, DIM_Date.sql and FACT_InternetSales.sql, plus a budget workbook labeled budget_2021.xlsx, and the README states the analysis timeframe covers two years of data. (github.com) Repository metadata shows 18 commits and a small public footprint (1 star, 0 forks), and the files tree contains a screenshots folder and episode‑style SQL script files that document the extraction/cleansing steps. (github.com) The underlying AdventureWorksDW2019 sample warehouse used by the project contains FactInternetSales and the dimensional tables (DimProduct, DimCustomer, DimDate) commonly used to calculate revenue, units and time series trends, making it suitable for driver‑based decomposition. (learn.microsoft.com) DimProduct in AdventureWorks exposes fields such as ListPrice and StandardCost and FactInternetSales holds line‑level sales measures, which the project’s SQL + Power BI workflow can map to CPG KPIs like revenue, gross margin and budget variance for month‑over‑month analysis. (dataedo.com) The README credits guidance from Ali Ahmad and includes named deliverables (dashboard overview, customer and product filters, KPI vs budget), indicating the repository is structured to demonstrate business questions and acceptance criteria valuable for FP&A storytelling and variance root‑cause work. (github.com) Ruth Chika’s GitHub profile shows other Power BI/SQL projects (for example a CRM sales‑pipeline analysis and a digital readiness dashboard), suggesting this release is part of a small portfolio of guided, reproducible analytics builds. (github.com)
