Farm Sensor and Weather Data Pipeline on Delta Lake

Added Jun 2025 3 design docs

Agricultural decisions depend on combining slow-moving data, historical weather records and seasonal yield files, with fast-moving field sensor readings, yet these arrive in different formats on different rhythms. A unified pipeline that treats batch and streaming as one system lets agronomists see current field conditions in the context of history rather than in isolation.

The intern creates that unified pipeline on Databricks. Batch ingestion loads CSV and JSON datasets, weather archives and sensor exports, using Spark, while Structured Streaming continuously ingests simulated real-time sensor readings. Both paths write to Delta Lake tables refined through a Medallion Architecture: Bronze preserves raw inputs, Silver standardizes units, timestamps, and sensor identifiers with Python and SQL transformations, and Gold produces analysis-ready aggregates such as daily readings per field joined with weather conditions. Databricks SQL dashboards visualize the curated Gold layer, giving users charted insights into moisture trends, temperature patterns, and anomalies worth investigating.

The project teaches the intern how batch and streaming ingestion coexist in one lakehouse, how schema standardization turns messy multi-source data into a coherent model, and how to carry raw files all the way through to dashboards stakeholders can use.

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