Real-Time Streaming Pipeline for Energy Sector Sensor Data

Added Jun 2025 3 design docs

Energy companies operate turbines, substations, and smart meters that emit telemetry every second, but the value of those readings evaporates when they take hours to reach analysts. Grid operators need infrastructure that ingests high-throughput sensor streams, detects problems as they happen, and still keeps a queryable history for engineers and planners. The intern designs and implements that pipeline end to end. Kafka handles high-volume ingestion from simulated sensor fleets, Spark Streaming applies windowed aggregations and fault-tolerant transformations in Python, and results land in HDFS for durable distributed storage. Hive exposes the data as queryable tables while Presto enables fast federated analytics across sources, and Airflow orchestrates the whole workflow with dynamic DAG scheduling, retries, and backfills so the pipeline runs reliably without hand-holding. Along the way the intern learns the core patterns of modern streaming architecture: fault-tolerant processing, watermarking and windowing, schema design for time-series telemetry, and production-grade orchestration. These are the skills that transfer directly to data engineering roles in energy, IoT, and any industry where data arrives as a stream.

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