Welcome to the Data Warehouse and Analytics Project!
This project demonstrates the implementation of a modern data warehouse architecture using a multi-layered approach (Bronze, Silver, Gold) to enable efficient, scalable, and insightful Business Intelligence and Analytics.
This project showcases a structured pipeline where raw data from enterprise sources like CRM and ERP systems is transformed and curated through multiple layers into business-ready data. The final data layer supports downstream applications such as dashboards, analytics, and machine learning.
The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers:
- Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.
- Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.
- Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.
To build a robust and scalable data pipeline that ingests, processes, and serves data for analytical and reporting purposes.
-
Sources: CRM and ERP Systems
- File Type: CSV
- Interface: Folder-based ingestion
-
Data Warehouse Architecture (SQL Server):
- Bronze Layer: Raw data (no transformations)
- Load Type: Batch Processing, Full Load, Truncate & Insert
- Object Type: Tables
- Data Model: None (as-is)
- Silver Layer: Cleaned and standardized data
- Transformations: Data cleansing, standardization, derived columns, enrichment
- Object Type: Tables
- Data Model: None (as-is)
- Gold Layer: Business-ready data
- Transformations: Aggregations, business logic, integrations
- Object Type: Views
- Data Model: Star schema, flat tables, aggregated tables
- Bronze Layer: Raw data (no transformations)
-
Consumer Layer:
- BI & Reporting
- Data Analytics
- Predictive Analytics
The cleaned and business-ready data supports:
- Business Intelligence: Interactive dashboards and reports
- Descriptive & Diagnostic Analytics: Trend and pattern discovery
- Predictive Analytics: ML/AI models using historical data
Tools such as Power BI, Tableau, or Python (Pandas, Scikit-learn) can be plugged into the Gold Layer to drive actionable insights.
This project is licensed under the MIT License.
I'm Lemgo Lawrence, a geospatial and data enthusiast with a strong foundation in remote sensing, machine learning, and spatial analytics. I’m passionate about translating complex data into practical insights for urban and environmental problem-solving.