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End-to-End DWBI Project: Real-Life Data Integration with Snowflake, Python, SQL & PowerBI

A Real-Life Data Warehouse and Business Intelligence (DWBI) project! In this repository, we'll walk you through the complete process of loading data into Snowflake using Python, performing powerful SQL queries, and visualizing data through a dynamic PowerBI dashboard.

Simplified DWBI Architecture

Project Overview

This project is designed to provide hands-on experience with real-life data integration techniques using Snowflake, Python, SQL, and PowerBI. We will cover everything from designing an ER diagram to generating insightful reports.

Project Structure

The project is structured into several key components:

  • On Prem DB: Includes tasks like extracting CSV files, generating historical sales data, and creating dimensional data.
  • Facts: Focuses on Snowflake dimensions and Power BI dashboards.
  • Ad hoc Query: Handles Excel reference data and other queries.

Key Learnings

Throughout this project, you will learn about:

  • Data Model: Project flow, ERD, table structure, star and snowflake schema, and relationships.
  • Python: Libraries, Pandas, reading from Excel, CSV generation, and test data generation.
  • Snowflake: Account creation, basics, object creation, loading data, and connectivity with PowerBI.
  • SQL: Writing queries, handling scenarios, and using the WITH clause.
  • Power BI: Power Query, blueprint creation, data modeling, and report generation.

Tables and Entities

The project revolves around an offline retail organization that sells thousands of products across hundreds of retail stores. The key entities/tables include:

  • Product
  • Store
  • Date
  • Customer
  • Sales/Factorders
  • Loyalty Program

Final Dashboard

Technology Used

  • Programming Language - Python
  • SQL
  • SnowFlake
  • SnowSQL
  • PowerBI
  • Excel

What You'll Learn

In this comprehensive End-to-End DWBI Project, you will gain hands-on experience and in-depth knowledge of the following key components:

  1. ER Diagram Design:

    • Understand the basics of Entity-Relationship (ER) diagrams.
    • Learn how to design an ER diagram for a data warehouse project.

  2. Star and Snowflake Schema:

    • Grasp the concepts of star and snowflake schema designs.
    • Learn how to implement these schemas to optimize your data warehouse.

  3. Test Data Generation Using Python:

    • Generate realistic test data using Python for your data warehouse.
    • Learn techniques to automate the creation of large datasets.

  4. Snowflake Configuration and Object Creation:

    • Set up and configure your Snowflake environment.
    • Create necessary objects such as databases, schemas, tables, and views in Snowflake.

  5. Loading Data into Snowflake:

    • Master the process of loading data into Snowflake using Python scripts.
    • Understand different data loading techniques and best practices.

  6. Scenario-Based SQL Queries:

    • Write and execute complex SQL queries to manipulate and retrieve data.
    • Learn to handle various real-life scenarios using SQL in Snowflake.

  7. Report Blueprint Creation:

    • Design blueprints for reports to ensure clear and effective data visualization.
    • Plan your reports to convey the right information to stakeholders.

  8. Report Generation Using PowerBI:

    • Create dynamic and interactive reports using PowerBI.
    • Learn to connect PowerBI to your Snowflake data and build insightful dashboards.

Getting Started

To get started with this project, clone the repository and follow the step-by-step instructions provided in the respective sections.

git clone https://github.com/your-repository/end-to-end-dwbi-project.git

Install the required Python libraries:

pip install -r requirements.txt

Python PowerBI Snowflake Microsoft Excel MySQL