Energy Consumption Batch Processing and Reporting Pipeline

Utilities and large facilities collect meter readings from thousands of households and industrial sites, and the questions that matter, such as which sites are trending upward, where peak loads occur, and how weekday usage differs from weekends, require processing volumes of data that outgrow a single machine. Batch analytics on a distributed stack is the industry-standard answer. In this project the intern designs that stack from ingestion to report. Simulated household and industrial consumption records are streamed through Kafka, landed in HDFS for scalable storage, and processed in scheduled batches with PySpark jobs that compute usage patterns, peak periods, and per-segment summaries. Python scripts tie the workflow together, trigger the processing runs, and export findings as CSV reports that an operations team could consume directly. The intern must think about partitioning, job efficiency, and verifying results rather than just producing numbers. The project builds practical big-data competence in the energy domain: designing a batch pipeline, writing distributed transformations, and delivering trustworthy summary reports, all skills that transfer directly to entry-level data engineering work.