Data Warehouse Design
& ETL Optimization
Building a high-performance Star Schema Data Warehouse in SQL Server with an automated Python ETL pipeline and interactive Power BI analytics.
Project Overview
A comprehensive approach to solving enterprise analytical challenges using modern data engineering practices.
The Problem
Traditional OLTP systems are not optimized for complex analysis. Direct reporting leads to performance bottlenecks, data inconsistency, and lack of historical context.
The Solution
Implemented a centralized Data Warehouse with a robust Star Schema and an optimized ETL framework for efficient data processing and visualization.
Optimization
Utilized incremental loading, composite indexing, and query pre-aggregation to achieve up to 70% faster reporting performance.
Project Deliverables
Access the core documentation including research papers, review presentations, and abstracts.
Architecture & Workflow
Detailed technical breakdown of the data journey from source to dashboard.
Extraction Phase
Gathering heterogeneous data from CSV files and SQL Server operational sources using specialized Python connectors.
Staging & Transformation
Data cleansing, deduplication, and normalization in a dedicated staging layer. Applying business logic and referential integrity checks.
Warehouse Loading
Loading dimensions first, followed by fact tables. Implementing incremental loads to minimize processing time for large datasets.
BI Visualization
Connecting Power BI to the optimized Star Schema to generate dynamic, real-time dashboards for KPI tracking.
Technology Stack
The tools and frameworks powering this project.