Student Clickstream Analytics on the Hadoop Ecosystem

Added Jun 2025 3 design docs

Online learning platforms capture every click, video view, and quiz attempt, signals that could flag struggling students within hours, yet most institutions still analyze engagement weeks later, after the moment for intervention has passed. Acting on learning data in real time requires an architecture that handles both continuous streams and heavyweight historical analysis. The intern engineers that system on the open-source big data stack. Clickstream and assessment events are ingested through Kafka and Flume, Spark Streaming processes them into near-real-time engagement metrics while batch Spark jobs compute deeper historical analyses, and results persist to HDFS with Hive providing warehouse-style querying. Airflow orchestrates the interlocking batch and streaming workflows with dependencies, retries, and schedules, and Presto enables fast federated queries across the stored datasets for ad-hoc questions. Prometheus scrapes pipeline and cluster metrics, with Grafana dashboards displaying both system health and live student engagement indicators such as session activity and assessment completion trends. The project demonstrates rare breadth across the Hadoop-ecosystem toolchain, dual-mode processing, orchestration, federated querying, and observability, proving the intern can integrate many moving parts into one dependable analytics platform.

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