Banking_Data_Pipeline/readme.md

Banking Transaction Pipeline
(Python • Spark • S3)

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

1. About The Project
   • Key Features
2. Architecture
   • Bronze (Raw)
   • Silver (Clean & Validated)
   • Gold (Curated & Analytics-Ready)
3. Data Quality & Validation
4. Outputs
5. Roadmap
6. License
7. Connect With Me

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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

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

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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

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

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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:

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

License

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

💬 Connect With Me

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