All Technologies Used
Motivation
The ETL process receives data from multiple sources, some of which provide incomplete or inconsistent information. The goal was to ensure that the ETL process delivers the most complete, accurate, and consistent data without introducing empty or incorrect values from less detailed sources.
Main Challenges
Data from different sources varied in terms of completeness and detail. Incomplete data was being loaded into the data warehouse, potentially affecting data quality and reliability.
In certain cases, more detailed attribute values were overwritten by empty or less detailed values, leading to duplication and redundancy in the data warehouse.
Key Features
- Survivorship Matrix: A clear set of rules for deciding whether to keep or overwrite data attributes based on source priority, which improved data quality and consistency.
- Data Deduplication: Eliminated redundant data overwriting, ensuring that only the most reliable and complete data was loaded into the data warehouse.
- Scalability: The solution is easily scalable, as new attribute priorities can be added without requiring changes to the core ETL process.
- Performance Optimization: Achieved the goal of reducing the ETL process running time to under 5 minutes, significantly improving operational efficiency.
Our Approach
Project Impact
Improved Data Quality: The solution ensured that only the most complete and accurate data was loaded into the data warehouse, improving the overall quality and reliability of the information.
Enhanced Flexibility and Scalability: The Survivorship Matrix made it easy to adjust priority rules and scale the system without disrupting the ETL process.
Optimized Performance: The reduction in ETL processing time from over 30 minutes to less than 5 minutes led to increased operational efficiency and responsiveness.