📊 SQL Data Analytics Pipeline

📝 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.

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🔄 Project Workflow

1️⃣ Data Warehouse (DWH) & ETL 📂

🟢 Objective: Build a Data Warehouse using SQL Server, implementing ETL (Extract, Transform, Load) 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: Designed using Star Schema with fact and dimension tables.
• ⚙ ETL Pipelines: Batch processing strategies for data ingestion and transformation.
• 📊 Final Output: Clean, structured data stored in the Gold Layer for analytics.

🔗 Reference: Data Warehouse Project

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2️⃣ Exploratory Data Analysis (EDA) 🔍

🟢 Objective: Uncover insights, trends, and anomalies in the dataset using SQL queries.

🛠 Approach: Uses the Gold Layer from the DWH to perform dimension and measure analysis.

📌 Key Steps:

• 🏷 Dimension Analysis: Understanding segmentation (e.g., customer demographics, product categories).
• 📊 Measure Exploration: Computing key metrics (e.g., revenue, total sales, average price).
• 📈 Ranking & Trend Analysis: Identifying top/bottom-performing entities using SQL functions.

🔗 Reference: EDA Project

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3️⃣ Advanced Analytics 📈

🟢 Objective: Perform complex analytical operations to extract deeper business insights.

🛠 Approach: Uses advanced SQL techniques, including trend analysis, cumulative metrics, segmentation, and performance evaluation.

📌 Key Steps:

• ⏳ Time-Series Analysis: Identifying changes over time using GROUP BY, DATETRUNC, and LAG.
• 📊 Cumulative Metrics: Running totals, moving averages, and YoY comparisons.
• 🏆 Performance Analysis: Ranking, category contribution analysis, and part-to-whole evaluations.
• 🔍 Segmentation & Reporting: Customer segmentation with CASE WHEN, product performance evaluation.

🔗 Reference: Advanced Analytics Project

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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: Data architecture visualization.
• 🐙 Git & GitHub: Version control & collaboration.

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📜 License

This project is licensed under the MIT License.