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-
Banking Transaction Pipeline (Python • Spark • S3)
+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.
-
+
+
+
-
-
+ 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**
+
+---
@@ -44,12 +51,17 @@
-
+---
-## 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
@@ -63,11 +75,11 @@ This project simulates a **banking transaction data pipeline** using **Python +
-## 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
@@ -78,7 +90,7 @@ The pipeline follows a lakehouse pattern where each layer has a clear responsibi
---
-### Silver (Clean & Validated)
+## Silver (Clean & Validated)
**Purpose**
- Standardize schema and datatypes
@@ -92,7 +104,7 @@ The pipeline follows a lakehouse pattern where each layer has a clear responsibi
---
-### Gold (Curated & Analytics-Ready)
+## Gold (Curated & Analytics-Ready)
**Purpose**
- Create business-friendly datasets and aggregations for analytics and BI
@@ -102,9 +114,6 @@ The pipeline follows a lakehouse pattern where each layer has a clear responsibi
- Account/customer-level summaries
- Error/invalid transaction metrics
-(back to top)
-
-
### Notes
@@ -112,13 +121,12 @@ The pipeline follows a lakehouse pattern where each layer has a clear responsibi
- **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
+# Data Quality & Validation
The pipeline applies checks to prevent bad data from reaching curated datasets.
@@ -132,7 +140,6 @@ The pipeline applies checks to prevent bad data from reaching curated datasets.
These checks keep the Silver and Gold layers consistent and trustworthy for downstream analytics.
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@@ -149,11 +156,9 @@ s3:///
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
(back to top)
@@ -167,7 +172,6 @@ Downstream analytics and metric definitions
- Add CDC-style ingestion simulation
-(back to top)
## License