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<h3 align="center">Banking Transaction Pipeline (Python • Spark • S3)</h3> <h2 align="center">Banking Transaction Pipeline <br> (Python • Spark • S3)</h2>
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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. <img height="250" src="https://git.camcodes.dev/Cameron/Data_Lab/raw/branch/main/images/Banking.jpg" alt="Banking pipeline diagram" />
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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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## 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 ### Key Features
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## Architecture # Architecture
The pipeline follows a lakehouse pattern where each layer has a clear responsibility. The pipeline follows a lakehouse pattern where each layer has a clear responsibility.
### Bronze (Raw) ## Bronze (Raw)
**Purpose** **Purpose**
- Store transactions “as received” with minimal transformation - Store transactions “as received” with minimal transformation
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### Silver (Clean & Validated) ## Silver (Clean & Validated)
**Purpose** **Purpose**
- Standardize schema and datatypes - Standardize schema and datatypes
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### Gold (Curated & Analytics-Ready) ## Gold (Curated & Analytics-Ready)
**Purpose** **Purpose**
- Create business-friendly datasets and aggregations for analytics and BI - Create business-friendly datasets and aggregations for analytics and BI
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- Account/customer-level summaries - Account/customer-level summaries
- Error/invalid transaction metrics - Error/invalid transaction metrics
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### Notes ### Notes
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- **Silver** should contain cleaned and validated records - **Silver** should contain cleaned and validated records
- **Gold** should contain curated outputs ready for analytics and BI - **Gold** should contain curated outputs ready for analytics and BI
For deeper implementation details, see the code in this repo.
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## Data Quality & Validation # Data Quality & Validation
The pipeline applies checks to prevent bad data from reaching curated datasets. 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. These checks keep the Silver and Gold layers consistent and trustworthy for downstream analytics.
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Gold-layer datasets are structured to support: Gold-layer datasets are structured to support:
Business intelligence tools (Tableau / Power BI) - Business intelligence tools (Tableau / Power BI)
- Ad-hoc querying (Spark SQL / DuckDB)
Ad-hoc querying (Spark SQL / DuckDB) - Downstream analytics and metric definitions
Downstream analytics and metric definitions
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- Add CDC-style ingestion simulation - Add CDC-style ingestion simulation
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## License ## License