Banking_Data_Pipeline/readme.md

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<h3 align="center">Banking Transaction Pipeline (Python • Spark • S3)</h3>

  <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.
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<details open>
  <summary>Table of Contents</summary>
  <ol>
    <li>
      <a href="#about-the-project">About The Project</a>
      <ul>
        <li><a href="#key-features">Key Features</a></li>
      </ul>
    </li>
    <li>
      <a href="#architecture">Architecture</a>
      <ul>
        <li><a href="#bronze-raw">Bronze (Raw)</a></li>
        <li><a href="#silver-clean--validated">Silver (Clean & Validated)</a></li>
        <li><a href="#gold-curated--analytics-ready">Gold (Curated & Analytics-Ready)</a></li>
      </ul>
    </li>
    <li>
      <a href="#getting-started">Getting Started</a>
      <ul>
        <li><a href="#prerequisites">Prerequisites</a></li>
        <li><a href="#installation">Installation</a></li>
        <li><a href="#configuration">Configuration</a></li>
      </ul>
    </li>
    <li><a href="#usage">Usage</a></li>
    <li><a href="#data-quality--validation">Data Quality & Validation</a></li>
    <li><a href="#outputs">Outputs</a></li>
    <li><a href="#roadmap">Roadmap</a></li>
    <li><a href="#contributing">Contributing</a></li>
    <li><a href="#license">License</a></li>
    <li><a href="#contact">Contact</a></li>
    <li><a href="#acknowledgments">Acknowledgments</a></li>
  </ol>
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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.

### Key Features

- **Batch ingestion** of banking-style transaction data into an S3-backed Bronze layer
- **Bronze → Silver → Gold** lakehouse-style architecture
- **Data validation gates** (required fields, schema enforcement, duplicates, constraints)
- **Curated datasets** designed for BI and ad-hoc analytics
- Designed with **analytics engineering principles**: reliable outputs, repeatability, clear modeling

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## Architecture

The pipeline follows a lakehouse pattern where each layer has a clear responsibility.

### Bronze (Raw)

**Purpose**
- Store transactions “as received” with minimal transformation

**Why it matters**
- Preserves an auditable source of truth
- Enables reprocessing into Silver/Gold without re-ingesting from the source

---

### Silver (Clean & Validated)

**Purpose**
- Standardize schema and datatypes
- Validate records and isolate invalid data
- Deduplicate and normalize for analysis

**Typical transformations**
- Datatype casting (timestamps, numeric amounts)
- Standardized column names and formats
- Deduplication rules (e.g., transaction_id collisions)

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### Gold (Curated & Analytics-Ready)

**Purpose**
- Create business-friendly datasets and aggregations for analytics and BI

**Example outputs**
- Daily transaction counts & totals
- Account/customer-level summaries
- Error/invalid transaction metrics

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### Notes

- **Bronze** should contain raw ingested data (audit layer)
- **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.

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---

## Data Quality & Validation

The pipeline applies checks to prevent bad data from reaching curated datasets.

**Common checks include:**
- Required fields (e.g., `transaction_id`, `account_id`, `amount`, `timestamp`)
- Schema enforcement (consistent datatypes between runs)
- Duplicate detection (e.g., `transaction_id` collisions)
- Value constraints (e.g., amounts must be non-negative)
- Timestamp parsing and validation
- Quarantine routing for invalid records (optional, stored under `errors/`)

These checks keep the Silver and Gold layers consistent and trustworthy for downstream analytics.

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---

## Outputs

**Example S3 layout:**
```text
s3://<bucket>/
  bronze/banking/
  silver/banking/
  gold/banking/
  errors/banking/
```

Gold-layer datasets are structured to support:

Business intelligence tools (Tableau / Power BI)

Ad-hoc querying (Spark SQL / DuckDB)

Downstream analytics and metric definitions

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Roadmap

 Add orchestration (Airflow / Dagster)

 Implement incremental processing and partitioning

 Add automated pipeline health checks (row counts, null rates, duplicates)

 Add unit tests for validation logic

 Add monitoring, alerting, and run logs

 Add CDC-style ingestion simulation

See the open issues
 for a full list of proposed features and known issues.

<p align="right">(<a href="#readme-top">back to top</a>)</p>License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Cameron Seamons
Ogden, Utah
Email: CameronSeamons@gmail.com

LinkedIn: linkedin_username

Project Link: https://github.com/github_username/repo_name

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