📊 SQL-Project-E-Commerce-Case

📝 Overview

This repository presents a complete SQL-based Data Analytics Pipeline, progressing from Data Warehousing to Exploratory Data Analysis (EDA) and finally to Advanced Analytics. The goal is to create a structured, efficient, and insightful SQL-driven analytical workflow.

Dataset used: Brazilian E-Commerce Public Dataset by Olist ➝ Kaggle Link

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Project Workflow

1️⃣ Data Warehouse (DWH) & ETL 📂

🟢 Objective: Build a Data Warehouse using SQL Server, implementing ETL processes.

🛠 Approach: Leverages the Medallion Architecture (Bronze, Silver, and Gold layers) to store and transform raw data into business-ready insights.

📌 Key Steps:

• 🏛 Data Architecture: Bronze Layer for raw data ingestion and storage, Silver Layer for cleaned, validated, and standardized data. Gold Layer for business-ready, aggregated data optimized for analytics.
• ⚙ ETL Pipelines: Batch processing strategies for data ingestion and transformation.
• 📊 Final Output: Clean, structured data stored in the Gold Layer using Star Schema with fact and dimension views ready for analytics.

💼 Business Value:

• A Data Warehouse serves as the single source that consolidates disparate data sources into a unified, consistent format. This enables businesses to perform cross-functional analysis, identify trends that span multiple departments, and generate insights.
• The structured approach demonstrated in this project - with bronze, silver, and gold layers - reflects how enterprises manage data quality and accessibility at scale and shows the technical infrastructure required.

🔗 Reference: DWH Project

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2️⃣ Exploratory Data Analysis (EDA) 🔍

🟢 Objective: Investigate the dataset to discover patterns, spot anomalies, make statistic analysis and extract meaningful insights.

🛠 Approach: Use the Gold Layer from the DWH to perform the analysis exploring 5 strategic analytical dimensions.

📌 Key Objects:

• 🗄️Database Profiling: Understanding data structure, quality, and storage patterns.
• 📆Temporal Analysis: Uncovering time-based trends and seasonality.
• 🔢Key metrics Exploration: Identifying distributions, outliers and segmentation.
• ⚖️Magnitude Analysis: Quantifying business entity performance across multiple metrics.
• 🏆Rank Analysis: Identify top/bottom-performing entities and establish competitive benchmarks.

💼 Business Value:

• Exploratory Data Analysis serves as the analytical bridge between data infrastructure and business intelligence. Through systematic investigation across those multiple dimensions organizations can identify hidden opportunities, detect emerging risks, and understand the underlying drivers of their business performance.
• This project shows how it is possible to systematically explore business datasets by focusing on distinct analytical areas rather than conducting random exploratory queries

🔗 Reference: EDA Project

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3️⃣ Advanced Analytics 📈

🔜 Next Step

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🔧 Technologies Used

• 🗄 SQL Server: Data processing & querying.
• 📂 CSV Datasets: Source files for ETL processes.
• 📊 SSMS: SQL Server Management Studio for database interaction.
• 🖼 DrawIO & Power Point: Data architecture visualization.
• 🐙 GitHub: Version control & collaboration.

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📜 License

This project is licensed under the MIT License.