Big Data Architectures for Large-Scale Digital Learning Platforms

Abstract

Large-scale digital learning platforms generate petabyte-scale interaction data streams from millions of concurrent learners -- video watch events, clickstream logs, assessment responses, forum interactions, and real-time collaboration metadata -- that must be ingested, processed, and analysed within seconds to support real-time adaptive learning, early warning systems, and live instructor dashboards. Designing big data architectures that satisfy the simultaneously demanding requirements of high-throughput stream ingestion, sub-second query latency, cost-efficient cold storage, and GDPR-compliant data governance is a significant engineering challenge for learning platform operators. This paper proposes the Learning Platform Big Data Architecture Framework (LPBDAF), a systematic evaluation of five data architecture patterns -- Lambda, Kappa, Delta Lake, data mesh, and federated learning architecture -- across six performance and governance benchmarks on simulated and real platform data at scales of 100K to 10M concurrent learners. LPBDAF introduces the Learning Platform Architecture Score (LPAS) integrating throughput, latency, cost, and compliance. Key results: Delta Lake achieves the highest LPAS (0.912) with 2.4 million events/second ingest throughput and 180 ms p99 query latency; federated learning architecture achieves the highest GDPR compliance score (0.964) enabling cross-institutional analytics without raw data transfer; Kappa achieves the lowest total cost of ownership (TCO) at 0.18 USD per 1,000 learner-hours. The framework provides architecture selection guidance for platform engineers.