Grid Telemetry Processing and Anomaly Reporting Suite

Added Nov 2025 3 design docs

Power grid operators watch millions of sensor readings for the early signs of trouble: a transformer running hot, a feeder with erratic voltage, a region where demand is drifting above forecast. The data engineering behind that vigilance is the subject of this project, built entirely as pipelines and reports with no web or mobile interface to hide behind. The intern simulates large-scale grid telemetry and streams it through Kafka into HDFS, establishing a durable, scalable landing zone. Batch PySpark jobs then process the accumulated readings, computing per-substation performance metrics, detecting anomalies such as outlier voltages and missing reporting intervals, and producing detailed analytics reports on grid health. Python scripts orchestrate the whole pipeline, automate data cleaning, and package the outputs as structured data files. The design is modular so a team can split ingestion, processing, and reporting work cleanly. This is a demanding, realistic data-engineering build: the intern practices stream ingestion, distributed storage layout, efficient batch computation, and anomaly detection logic, and finishes able to discuss the architecture of a production-style big-data platform in the energy sector.

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