Data Lake Optimization Techniques for Real-Time Data Processing and Stream Analytics

Abstract

This paper examines optimization techniques for data lakes focused on supporting real-time data processing and stream analytics. We propose a taxonomy of techniques—partitioning and pruning, file compaction with Z-ordering, transactionally consistent storage (Delta Lake) with upserts, and stream-side pre-aggregation—and evaluate their impact on latency, throughput, query performance, and storage cost. Using a synthetic but realistic case study modeled on a streaming telemetry workload, we quantify improvements across operational and analytical metrics. Results show substantial reductions in end-to-end latency, significant throughput gains, and lower analytical query times after applying optimizations. We discuss methodology, implementation considerations, evaluation results (table + graph), and future research directions including domain-specific models and adaptive, cost-aware optimization