Finance-Data-Engineering-Platform README

# Finance-Data-Engineering-Platform

A scalable financial data engineering platform built using Databricks, PySpark, SQL, and cloud-based ETL pipelines. This project demonstrates modern data engineering practices including Medallion Architecture, incremental data ingestion, transformation pipelines, dashboarding, and automated notifications.

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## Project Overview

This platform processes financial and banking-related data from multiple sources and transforms it into analytics-ready datasets for reporting and business insights.

The project follows a layered Medallion Architecture:

- Bronze Layer → Raw data ingestion
- Silver Layer → Cleaned and transformed data
- Gold Layer → Business-ready analytical datasets

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## Tech Stack

- Databricks
- PySpark
- SQL
- Delta Lake
- Azure Blob Storage
- ETL Pipelines
- Lakehouse Architecture
- Dashboarding & Reporting
- Python

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## Project Architecture

Source Systems
↓
Bronze Layer (Raw Ingestion)
↓
Silver Layer (Data Cleaning & Transformation)
↓
Gold Layer (Business KPIs & Analytics)
↓
Dashboards & Notifications

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## Features

- End-to-end ETL pipeline
- Incremental data loading
- Data transformation workflows
- Banking KPI generation
- Customer 360 analytics
- Risk analysis pipelines
- Automated email notifications
- Dashboard integration
- Metadata-driven architecture

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## Project Structure

```bash
Finance-Data-Engineering-Platform/
│
├── 00_Source_Files/
├── 01_Setup_Metadata/
├── 02_Source_to_Silver/
├── 03_Silver_to_Gold/
├── 04_Email_Notification/
└── 05_Dashboard/

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Key Modules

Source to Silver

• Raw data ingestion
• Secret scope setup
• Metadata-driven processing
• Bronze to Silver transformations

Silver to Gold

• Customer analytics
• Banking KPIs
• Branch performance analysis
• Risk customer summary
• Transaction analytics

Dashboard

• Business intelligence dashboards
• Financial reporting views

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Learning Outcomes

This project demonstrates:

• Real-world data engineering workflows
• Databricks notebook orchestration
• Delta Lake processing
• ETL pipeline design
• Financial analytics engineering
• Lakehouse architecture implementation

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Future Improvements

• Apache Airflow orchestration
• Real-time streaming with Kafka
• CI/CD integration
• Docker containerization
• AWS/Azure deployment
• Advanced monitoring & logging

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Author

Kishan Kumar

GitHub: https://github.com/kishangithubkumar