An end-to-end SQL analysis of the Olist Brazilian e-commerce dataset, covering data validation, exploratory analysis, customer and order behavior, delivery performance, product demand, payment activity, seller performance, and business recommendations.
This project analyzes the Olist Brazilian e-commerce marketplace using SQL to examine how customers, orders, products, payments, reviews, delivery operations, and sellers interact across the marketplace.
The analysis follows a structured workflow:
Validate → Explore → Interpret → Recommend
- Validate data quality and relational integrity
- Explore customer, order, product, payment, review, and seller behavior
- Identify operational and commercial patterns
- Translate analytical findings into actionable business recommendations
| Area | Focus |
|---|---|
| Customers | Geographic distribution and customer concentration |
| Orders | Order status and fulfillment activity |
| Reviews | Review scores and response intervals |
| Delivery | Delivery performance and delays |
| Products | Category demand and monetary contribution |
| Payments | Payment methods, values, and payment differences |
| Sellers | Order volume and geographic concentration |
- 96,478 orders were recorded as delivered, making delivery the dominant order status.
- Customer activity is strongly concentrated geographically, with São Paulo representing the largest customer market.
- Credit card is the dominant payment method by customer activity and payment value.
cama_mesa_banhorecorded the highest order volume among the product categories analyzed.- High order volume did not always correspond to the highest monetary contribution across categories.
- Seller activity was concentrated among a relatively small group of high-order-volume sellers.
- Significant delivery delays were identified within delivered orders, highlighting potential operational and customer-experience concerns.
➡️ View detailed Business Findings, Insights & Recommendations
The dataset was examined for:
- Table structures and record counts
- Data types and formatting consistency
- Missing and blank values
- Duplicate records
- Referential integrity
- Logical data-quality issues
➡️ View Data Cleaning & Validation
SQL was used to investigate:
- Customer and geographic distribution
- Order-status patterns
- Customer review behavior
- Delivery performance and delays
- Product-category demand
- Payment behavior
- Seller performance and concentration
➡️ View Exploratory Data Analysis
The analytical results were translated into business-focused findings and recommendations across:
- Customer concentration
- Delivery operations
- Product demand
- Payment behavior
- Additional charges
- Seller performance
➡️ View Business Findings & Recommendations
Selected screenshots from the SQL exploration workflow:
| Tool | Purpose |
|---|---|
| SQL | Data validation, exploration and analysis |
| DuckDB | Analytical database engine |
| DBeaver | SQL development environment |
| Microsoft Word & Markdown | Analytical documentation |
The project uses the Olist Brazilian E-Commerce public dataset, obtained through Kaggle's Olist organization.
The dataset contains relational tables covering customers, orders, products, sellers, payments, reviews, geolocation, and product-category translations.
| Table | Records |
|---|---|
customers |
99,441 |
geolocation |
1,000,163 |
order_items |
112,650 |
order_payments |
103,886 |
order_reviews |
99,224 |
orders |
99,441 |
products |
32,951 |
sellers |
3,095 |
product_category_translation |
71 |
Olist-Ecommerce-SQL-Analysis/
│
├── data/
│
├── sql/
│ ├── 01_data_validation/
│ ├── 02_exploration_analysis/
│ └── ...
│
├── documentation/
│ ├── data_cleaning_and_validation.md
│ ├── exploration_data_analysis.md
│ └── business_findings_insights_and_recommendations.md
│
├── results/
│ └── screenshots/
│
├── assets/
│
└── README.md
The project was developed in DBeaver using DuckDB, with SQL serving as the primary language throughout the validation and analytical workflow.
This project demonstrates how SQL can be used beyond query writing to move from data quality assessment to business analysis.
The final analysis connects relational e-commerce data to questions around:
- Customer behavior
- Geographic concentration
- Order fulfillment
- Customer experience
- Product demand
- Payment behavior
- Seller performance
- Pricing and additional charges
The result is a documented SQL analysis that translates raw marketplace data into business-relevant findings and recommendations.
Data Analyst | Business Intelligence | SQL | Python | Excel | Tableau
I enjoy transforming raw and complex datasets into meaningful insights, building analytical solutions, and communicating findings clearly to support data-driven decision-making.
- GitHub: Cephasia
- LinkedIn: Opeyemi Peter
- Email: Send me an email
Thank you for taking the time to explore this project.
If you find the analysis useful, feel free to explore the SQL queries and supporting documentation throughout the repository.