This podcast explains how data engineering serves as the vital foundation for converting messy, disorganized security logs into actionable intelligence. Because machine learning models require high-quality inputs, the source outlines a log ingestion pipeline that focuses on parsing, normalization, and feature extraction to ensure accurate analysis. It compares the roles of SIEMs and data lakes, highlighting the balance between real-time streaming for immediate detection and batch processing for historical threat hunting. The podcast also addresses the operational hurdles of managing large-scale telemetry, such as storage costs and data quality issues like missing fields or timing errors. Ultimately, the material emphasizes that while automated pipelines drive modern security analytics, human expertise remains essential for designing schemas and interpreting complex anomalies. Use examples, clarify terms, and ensure understanding.