Banking Transaction Pipeline (Python • Spark • S3)

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.

Table of Contents
  1. About The Project
  2. Architecture
  3. Getting Started
  4. Usage
  5. Data Quality & Validation
  6. Outputs
  7. Roadmap
  8. Contributing
  9. License
  10. Contact
  11. Acknowledgments
## 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) --- ### 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:/// 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.

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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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