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Data Warehouse and Analytics Project

Welcome to the Data Warehouse and Analytics Project repository! 🚀

This project demonstrates a comprehensive data warehousing and analytics solution, from building a data warehouse to generating actionable insights. Designed as a portfolio project highlights industry best practices in data engineering and analytics.

🏗️ Data Architecture

The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers:

Data Architecture

  1. Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV files into SQL Sever Database.
  2. Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.
  3. Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.

📖 Project Overview

This project involves:

  1. Data Architecture: Designing a Modern Data Warehouse Using Medallion Architecture Bronze, Silver, and Gold layers.
  2. ETL Pipelines: Extracting, transforming, and loading data from source systems into our warehouse.
  3. Data Modeling: Developing fact and dimension tables optimized for analytical queries.
  4. Analytics & Reporting: Creating SQL-based reports and dashboards for actionable insights.

🎯 In this project, the following skills are practiced:

  • SQL Development
  • Data Architecture
  • Data Engineering
  • ETL Pipline Development
  • Data Modeling
  • BI: Analytics & Reporting (Data Analytics)

🚀 Project Requirements

Building the Data Warehouse (Data Engineering)

Objective

Develop a modern data warehouse using SQL Server to consolidate sales data, enabling analytical reporting and informed decision-making.

Specifications

  • Data Sources: Import data from two source systems (ERP and CRM) provided as CSV files.
  • Data Quality: Cleanse and resolve data quality issues prior to analysis.
  • Intégration: Combine both sources into a single, user-friendly data model designed for analyticals queries.
  • Scope: Focus on the dataset only; historization of data is not required.
  • Documentation: Provide clear documentation of the data model to support both business stackeholders and analytics teams.

📊 BI: Analytics & Reporting (Data Analytics)

Objective

Develop SQL-based analytics to deliver datailed insights into :

  • Customer Behavior
  • Product Performance
  • Sales Trends

These insights empower stackeholders with key business metrics, enabling strategic decision-making

🛡️ Licence

This project is licensed under the MIT Licence. You are free to use, modify, and share this project with poper attribution.

🌟 About Me

Hi there! I'm Akanji Timothée Olanyi Olatundé, also know as ATOO. I'm an Data Professional, Analytics Developer & Data Engineer | BI | Python & SQL Expert, ETL, Cloud | Certified DataCamp | Health Tech Enthusiast on a mission to share my knowledge of Data subjects and use them as a lever to help improve the healthcare sector in Africa and around the world.

Let's stay in touch! Feel free to connect with me on the following platforms:

LinkedIn