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<br />
<h3 align="center">Banking Transaction Pipeline (Python • Spark • S3)</h3>
<h2 align="center">Banking Transaction Pipeline <br> (Python • Spark • S3)</h2>
<p align="center">
A Python-based Spark pipeline that ingests banking-style transactions into S3 and processes them through a Bronze → Silver → Gold architecture with data quality validation.
<br />
<p align="center">
<img height="250" src="https://git.camcodes.dev/Cameron/Data_Lab/raw/branch/main/images/Banking.jpg" alt="Banking pipeline diagram" />
</p>
</p>
</div>
A Python-based Spark pipeline that ingests banking transactions into S3.
Bronze → Silver → Gold architecture with data quality validation.
> [!NOTE]
> This project is intended to demonstrate **analytics engineering and lakehouse design patterns**
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<!-- TABLE OF CONTENTS -->
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</ol>
</details>
---
<!-- ABOUT THE PROJECT -->
## About The Project
# About The Project
This project simulates a **banking transaction data pipeline** using **Python + Apache Spark** with an **S3-backed data lake**.
It demonstrates how raw transactional data can be ingested, validated, transformed, and curated into analytics-ready datasets using a **Bronze → Silver → Gold** architecture.
## **Tech Stack:** Python, PySpark, Apache Spark, S3 storage
This project simulates a **banking transaction data pipeline** using **Python + Apache Spark** with an **S3-backed data lake**. It demonstrates how raw transactional data can be ingested, validated, transformed, and curated into analytics-ready datasets using a **Bronze → Silver → Gold** architecture.
### Key Features
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## Architecture
# Architecture
The pipeline follows a lakehouse pattern where each layer has a clear responsibility.
### Bronze (Raw)
## Bronze (Raw)
**Purpose**
- Store transactions “as received” with minimal transformation
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### Silver (Clean & Validated)
## Silver (Clean & Validated)
**Purpose**
- Standardize schema and datatypes
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### Gold (Curated & Analytics-Ready)
## Gold (Curated & Analytics-Ready)
**Purpose**
- Create business-friendly datasets and aggregations for analytics and BI
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- Account/customer-level summaries
- Error/invalid transaction metrics
<p align="right">(<a href="#readme-top">back to top</a>)</p>
### Notes
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- **Silver** should contain cleaned and validated records
- **Gold** should contain curated outputs ready for analytics and BI
For deeper implementation details, see the code in this repo.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
---
## Data Quality & Validation
# Data Quality & Validation
The pipeline applies checks to prevent bad data from reaching curated datasets.
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These checks keep the Silver and Gold layers consistent and trustworthy for downstream analytics.
<p align="right">(<a href="#readme-top">back to top</a>)</p>
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Gold-layer datasets are structured to support:
Business intelligence tools (Tableau / Power BI)
Ad-hoc querying (Spark SQL / DuckDB)
Downstream analytics and metric definitions
- Business intelligence tools (Tableau / Power BI)
- Ad-hoc querying (Spark SQL / DuckDB)
- Downstream analytics and metric definitions
<p align="right">(<a href="#readme-top">back to top</a>)</p>
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- Add CDC-style ingestion simulation
<p align="right">(<a href="#readme-top">back to top</a>)</p>
## License