ebuehiowen/fresh-produce-retail-analytics-sql-and-power-bi-capstone-project — explained in plain English
Analysis updated 2026-05-18
Study a worked example of turning raw retail transaction data into SQL views and a dashboard.
Use the SQL views as a template for answering revenue, waste, and customer behavior questions.
Reference the Power BI dashboard structure for building a multi-page executive report.
Read the findings report for an example of translating data analysis into business recommendations.
| ebuehiowen/fresh-produce-retail-analytics-sql-and-power-bi-capstone-project | 00kaku/gallery-slider-block | 04amanrajj/netwatch | |
|---|---|---|---|
| Stars | 0 | — | 0 |
| Language | — | JavaScript | Rust |
| Last pushed | — | 2021-05-19 | — |
| Maintenance | — | Dormant | — |
| Setup difficulty | moderate | easy | moderate |
| Complexity | 3/5 | 2/5 | 3/5 |
| Audience | pm founder | general | ops devops |
Figures from each repo's GitHub metadata at analysis time.
Requires PostgreSQL, DBeaver, and Power BI Desktop installed to explore the SQL views and dashboard.
This is a data analytics portfolio project that analyzes over 800,000 sales transactions from a fresh produce retailer, covering July 2020 through December 2023, to find what drives profit, where operations lose money, and how customers behave. The project sets out to answer 10 specific business questions grouped into three areas: financial analysis, operations and spoilage, and customer behavior. The work has three main parts. First, a PostgreSQL file called sql_views.sql that contains 10 SQL views built with multi-table joins, common table expressions, aggregations, and window functions to answer each business question. Second, a three page Power BI dashboard covering financial analysis such as revenue and profit, operations and spoilage such as waste costs and seasonal patterns, and customer behavior such as peak selling hours and returns, with date filtering and page navigation built in. Third, a written findings report summarizing the business insights and recommended actions. Among the specific questions the project tackles are which product categories generate the highest revenue versus the highest profit margin, how discounting affects quantity sold and total profit, which products cost the most in wasted stock, whether spoilage follows seasonal patterns, and which hours of the day see the busiest sales and highest return rates. The README shares some of its key findings directly: total revenue of 3.37 million dollars with a net profit of 1.03 million dollars, an annual waste cost of about 210,000 dollars from over 46,000 spoiled units, a median loss rate of 9.43 percent, and a peak sales hour of 4pm. It also reports that discounted items generate 75 percent less profit than items sold at full price. To use the project, a reader needs PostgreSQL and a tool like DBeaver to run the SQL views, plus Power BI Desktop to open the dashboard file and connect it to a database or CSV exports. The author describes it as part of a personal data analytics portfolio and asks that it be credited if referenced, though it uses no formal open source license.
A data analytics portfolio project using SQL and Power BI to analyze 800,000+ retail transactions and answer 10 business questions about profit, waste, and customer behavior.
No formal open source license, the author asks for credit if the project is referenced.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly pm founder.
This repo across BitVibe Labs
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