Real Time Security Analytics in Data Lakehouses Using Apache Iceberg and Event Streaming

Описание: Data lakehouses have revolutionized analytics, but securing petabyte-scale environments across multiple cloud providers presents unique challenges. This presentation demonstrates how organizations can leverage Apache Iceberg's time-travel capabilities alongside Apache Kafka streaming to build real-time security analytics directly within their lakehouse architecture. By treating security events as first-class data citizens, we enable behavioral analytics that detect anomalies across data access patterns, query behaviors, and pipeline executions.

Our implementation combines Iceberg's metadata layer with streaming security telemetry to create a self-defending lakehouse that identifies threats within seconds rather than hours. Using Apache Arrow for high-performance data exchange and Apache Spark for distributed processing, we've built an event-driven security framework that reduced detection time by 99% while processing over 10 billion events daily. The architecture integrates seamlessly with existing lakehouse tools, adding security intelligence without impacting analytical workloads.

A case study from a Fortune 500 financial services company showcases practical implementation, including schema evolution for security events, partitioning strategies for efficient threat hunting, and real-time dashboards built on streaming aggregations. We'll demonstrate how Iceberg's snapshot isolation enables forensic analysis without disrupting production workloads, while Arrow Flight accelerates security query performance. The framework addresses critical challenges including cross-cloud telemetry normalization, unified access control across engines, and automated threat response.

Attendees will learn actionable strategies for embedding security analytics into their lakehouse architecture, leveraging open-source technologies they already use. We'll share production-ready code examples, performance benchmarks, and lessons learned from securing multi-petabyte lakehouses across AWS, Azure, and GCP.