The Challenge

While global earthquake data is publicly available via the USGS, it is delivered as raw GeoJSON. For data analysts, this format presents several hurdles:

Complex Structure: Deeply nested data that is difficult to query with standard SQL.
High Velocity: Data is updated continuously, making manual collection impossible.
Lack of Insights: Raw data doesn't intuitively show geographic clusters, tsunami risks, or correlation between depth and magnitude.

The Solution

I built a robust, automated ELT (Extract, Load, Transform) pipeline that converts raw API responses into high-value business intelligence.

Technical Workflow:

Extraction: Automated fetching of live data from the USGS REST API.
Loading: Utilizing dlt (data load tool) to move raw GeoJSON into a Snowflake Data Warehouse, ensuring schema evolution is handled automatically.
Transformation: Using dbt (data build tool) to create modular, tested, and clean analytics-ready tables. This includes classifying alert levels (Green/Yellow/Orange/Red) and calculating risk metrics.
Orchestration: The entire workflow is managed by Apache Airflow, ensuring data is refreshed on a reliable schedule with built-in error handling.
Visualization: Final data is surfaced in an interactive dashboard for geographic and statistical analysis.

USGS Earthquakes — End to End Data Engineering Project

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