Smart Farm Data Platform with Delta Live Tables Orchestration

Added Jun 2025 3 design docs

Modern farms generate relentless data, soil sensors, weather stations, and drone imagery telemetry, but agronomy teams rarely have engineering support to turn those feeds into trustworthy analytics. The gap is architectural: raw sensor streams need ingestion, validation, transformation, and governance before anyone can responsibly base irrigation or planting decisions on them. The intern designs an end-to-end smart agriculture platform on Databricks and Spark. Sensor and drone data arrive through Kafka streams and Autoloader file ingestion, then flow through a Medallion Architecture implemented with Delta Live Tables: Bronze tables capture raw records, Silver tables apply DLT expectations that quarantine bad readings, and Gold tables aggregate field-level metrics. Databricks Workflows and Airflow orchestrate the pipelines on schedules and dependencies, while Databricks SQL dashboards visualize crop health indicators and yield predictions from the curated layer. The intern applies Unity Catalog for governance, secret scopes for credential handling, and cluster policies aimed at cost control. The project demonstrates command of the modern lakehouse stack, streaming plus batch ingestion, declarative pipelines with enforced data quality, dual orchestration tools, and security practices, applied to a domain where data quality has physical consequences.

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