# MAANG Unicorn Analytics

## Project Overview

This project builds an end-to-end data pipeline to analyze unicorn startup datasets for business and tech decision-makers (like MAANG companies).  
It covers everything from cloud storage to data cleaning, SQL integration, and fully interactive Power BI dashboards including advanced drill-through analysis.

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## Workflow

1. **Raw Data Collection**
   - Source startup data is stored in Azurite (simulated Azure Cloud) or MySQL database.

2. **Python Data Cleaning**
   - Python scripts load raw data, clean & transform it (fix monetary columns, standardize dates, derive fields like Decade).
   - Outputs are saved without local files—integration is cloud/DB-native.

3. **Data Storage**
   - Cleaned data is saved to Azurite blob storage, then imported into a MySQL database for secure, structured access.

4. **Further Cleaning (Python + MySQL)**
   - Python connects directly to MySQL for additional cleaning and feature engineering (removes formatting, adds analytical columns).

5. **Power BI Dashboarding**
   - Power BI connects directly to the cleaned MySQL tables (no CSVs).
   - Dashboards feature:
     - Overview KPIs
     - Visuals (bar, line, maps)
     - Drill-through pages for industry and company-level details

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## Features

- Fully automated ETL (extract-transform-load) pipeline
- No dependence on local intermediate files in the final product
- Designed for dynamic filtering and drill-through with minimal DAX complexity
- Supports business questions:
  - Which industries/countries have the most unicorns?
  - What are the top-valued startups?
  - How do unicorn trends shift by decade?
- MAANG/company-ready: clean, reliable, scalable

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## How to Run

1. Clone the repository
2. Install dependencies:


Thank You/Let’s Connect:
Name: Tanmay Sharma
Role: Data/Business Analyst
LinkedIn: https://www.linkedin.com/in/tanmay-sharma-800599373/
Github: https://github.com/Tanu272004/Unicorn-Analytics.git