Farm Sensor Data Ingestion Pipeline with Kafka and HDFS

Added Nov 2025 3 design docs

Modern farms generate continuous streams of readings for soil moisture, temperature, humidity, and equipment status, but the raw numbers are useless until they are reliably collected, stored, and summarized. Agritech companies solve this with streaming data platforms, and this project introduces that architecture at a scale a newcomer can build end to end. The intern writes Python producers that simulate realistic agricultural sensor streams and publish them to Kafka topics, then builds consumers that read the streams, validate and enrich the readings, and land them in HDFS for durable, distributed storage. On top of the stored data, Python analysis scripts compute aggregates such as average soil moisture per field, daily temperature ranges, and gaps that indicate sensor failures, producing summary files a farm manager could act on. The focus stays on the pipeline itself: partitioning topics sensibly, handling malformed messages, and organizing HDFS directories for easy downstream analysis. Through this big-data project the intern gains a concrete understanding of publish-subscribe streaming, distributed file storage, and pipeline design, which are foundational skills for any data engineering role.

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