Distributed Crop Yield Analysis Pipeline with PySpark

Added Nov 2025 3 design docs

Predicting and improving crop yields starts with unglamorous plumbing: field sensor readings and harvest records must be collected reliably, stored at scale, and distilled into trends a agronomist can act on. This project has the intern build that plumbing with the same tools used in commercial agritech data platforms. Simulated sensor and yield data is streamed through Kafka, capturing the continuous, event-driven nature of readings arriving from many fields. The stream lands in HDFS, which provides distributed storage for the growing dataset. PySpark batch jobs then process the records to generate yield summaries by crop and region, seasonal trend reports, and comparisons across growing conditions. Python scripts coordinate the workflow end to end, from ingestion through processing to exporting results as clean data files. The project is deliberately data-focused, with all outputs delivered as files and reports rather than interfaces. By completing it, the intern learns the streaming-to-batch architecture at the heart of big-data engineering, gains real PySpark experience with joins, aggregations, and window-style analysis, and can speak concretely about applying data infrastructure to agricultural problems.

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