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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, it highlights industry best practices in data engineering and 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.
  • Integration: Combine both sources into a single, user-friendly data model designed for analytical queries.
  • Scope: Focus on the latest dataset only; historization of data is not required.
  • Documentation: Provide clear documentation of the data model to support both business stakeholders and analytics teams.

BI: Analytics & Reporting (Data Analytics)

Objective

Develop SQL-based analytics to deliver detailed insights into:

  • Customer Behavior
  • Product Performance
  • Sales Trends

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


🧾 License

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


🌟 About Me

Hi there! I’m Likhith KM, an aspiring Data Scientist passionate about exploring data and sharing my learning journey. I’m on a mission to make data analytics and visualization exciting, insightful, and accessible to all! 🚀

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Building a modern data warehouse with SQL Server, including ETL processes, data modeling, and analytics.

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