DBT Cloud Data Transformation
Background
In the US, low-income communities of color face the highest degree of financial exclusion and exploitation. As income inequality rose in the 1980s, mainstream banks increased their focus on middle-income and higher-income depositors, shutting the depository accounts of people increasingly deemed too poor to bank. As mainstream financial exclusion accelerated, alternative financial services like payday lenders and pawn shops moved into low-income communities of color, leading to vast levels of “racialized” financial exploitation that persists today.
Details
SUDS collaborated with Giving Credit on a transformative data engineering project centered on their Amazon Redshift data warehouse. The primary objective was to develop sophisticated dbt transformations that reshaped raw data into analysis-ready formats, accelerated the creation of data visualizations in Giving Credit’s business intelligence platform, and established a foundation for future machine learning capabilities.
Deliverables
- A production-ready dbt transformations integrated with Giving Credit’s Redshift warehouse.
- Feature enhancements to Giving Credit’s application.
About the Partner
The organization is a social credit network that recognizes and capitalizes peer-to-peer lending income in low-income communities. By bringing transparency to informal lending networks and protecting peer-lenders against loss, it amplifies community finance and growth.
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