This project uses Databricks SQL to analyse coffee shop sales data and identify insights into product performance, revenue trends, and payment methods.
The analysis demonstrates SQL skills for querying and aggregating data, analysing sales performance, and presenting business insights through visualisations.
The dataset is stored in the data/ folder and contains 3,636 rows of coffee shop transaction data.
| Column | Data type | Description |
|---|---|---|
date |
DATE | Transaction date |
datetime |
TIMESTAMP | Transaction date and time |
cash_type |
STRING | Payment type |
card |
STRING | Card identifier, where available |
money |
DOUBLE | Transaction amount |
coffee_name |
STRING | Coffee product purchased |
The project explores the following business questions:
- Which coffee products generate the highest sales?
- How does revenue vary by payment method?
- How does total revenue change month by month?
- Which days have the highest transaction values?
- Which coffee products perform best in each month?
- Databricks SQL for data querying and analysis
- SQL for aggregations, filtering, grouping, and sales analysis
- GitHub for project documentation and version control
This project demonstrates how SQL can be used to transform transactional data into business insights, helping assess product performance, understand revenue patterns, and compare payment methods.