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End-to-end data pipeline built with Databricks using Medallion Architecture
End-to-end data pipeline built with Databricks using Medallion Architecture
This project demonstrates the design and implementation of a complete data pipeline for e-commerce analytics.
It transforms raw transactional data into business-ready insights using scalable data engineering practices.
Architecture
Architecture
The pipeline follows the Medallion Architecture:
Raw Layer: Ingested CSV data from source
Bronze Layer: Data standardization and ingestion metadata
Silver Layer: Data cleaning, validation, and joins
Gold Layer: Aggregated metrics for business analysis
Technologies Used
Technologies Used
Databricks
PySpark
Delta Lake
SQL
Databricks Jobs (orchestration)
Key Features
Key Features
End-to-end data pipeline (RAW → GOLD)
Data modeling using a fact table
Business metrics: revenue, orders, average order value
Automated workflow using Databricks Jobs
Dashboard for business insights
Dashboard Insights
Dashboard Insights
The dashboard provides visibility into:
Revenue trends over time
Product category performance
Customer behavior
Delivery performance
Business Impact
Business Impact
This pipeline enables:
Faster access to reliable data
Better decision-making through clear metrics
Scalable data processing workflows
My Role
My Role
Designed the data architecture
Developed the pipeline using PySpark
Implemented data transformations and joins
Created analytical datasets
Built dashboard and orchestrated workflows
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