Enterprise Data Warehouse Design

Existing data processing workflows were fragmented and inefficient, leading to long processing times and difficulties in generating consistent, reliable reports. There was a need for a centralized, structured data warehouse to:

• Consolidate data from various sources.
• Improve data quality and consistency.
• Reduce ETL processing times.
• Provide a reliable foundation for business intelligence and analytics.
• Enable easier data governance and lineage tracking.

A Medallion architecture (Bronze, Silver, Gold layers) was designed and implemented using SQL-based technologies. Key activities and outcomes included:

• Developed detailed data models for each layer, ensuring clear data lineage and transformation logic.
• Engineered and optimized over 15 ETL (Extract, Transform, Load) pipelines to ingest raw data (Bronze), cleanse and conform data (Silver), and create business-aggregated tables (Gold).
• Implemented data quality checks and validation rules within the ETL processes.
• Utilized SQL optimization techniques (indexing, query tuning, partitioning strategies) to enhance performance.
• Achieved a significant 40% reduction in overall data processing time compared to the previous system.
• The structured Gold layer directly feeds into reporting tools, simplifying analytics and dashboard creation.

The implementation of the Enterprise Data Warehouse based on the Medallion architecture successfully addressed the initial challenges. It provides a scalable, robust, and efficient platform for data management and analytics. The 40% reduction in processing time translates to faster insights and reduced computational costs. The standardized structure improves data trust and accessibility, empowering business users with reliable data for decision-making. This project established a strong foundation for future data initiatives within the organization.