Enterprise Data Warehouse Architecture & Optimization
The Challenge
An organization faced challenges with disorganized datasets spread across multiple platforms, leading to fragmented data sources. This resulted in inefficient query performance, high latency, and significantly rising Google Cloud Platform (GCP) costs.
Technical Stack
The Solution: Scalable BigQuery Modeling
I architected a centralized data warehouse using a multi-tiered approach to ensure data quality and performance, moving from disorganized raw data to highly optimized reporting structures.
Tiered Architecture
Implemented a Bronze-Silver-Gold (Landing-Staging-Production) flow to maintain clean lineage and robust data transformations.
Schema & Modeling
Designed a robust Star Schema. Utilized nested and repeated fields (STRUCT/ARRAY) for denormalization to minimize heavy JOIN operations.
Cost Control & Performance
Applied strict table partitioning by Date and clustering by key dimensions, drastically optimizing data scanning and reducing overhead.
Automated Pipeline
Integrated automated logic for daily ETL refreshes, ensuring the reporting layers are continually updated with zero manual intervention.
The Impact
- Efficiency & Cost: Achieved a 30% reduction in query costs and a significant decrease in processed data volume.
- Speed: Reduced report loading times from minutes to sub-second latency for executive-level dashboards.
- Scale: Built a robust framework capable of handling millions of rows daily without any performance degradation.