Security information and event management (SIEM) systems have long served as cornerstone infrastructure for organizational threat detection and response. However, the emergence of lakehouse architectures has introduced fundamentally different approaches to security data management. This comparison examines traditional SIEM platforms against Lakewatch, a modern lakehouse-based security analytics platform, highlighting architectural differences, operational implications, and strategic trade-offs.
Traditional SIEM systems employ proprietary, purpose-built architectures designed specifically for security event collection, enrichment, and alerting. These systems typically operate under a “collect and discard” operational model, where organizations face critical decisions about which data to retain based on storage costs and licensing constraints 1).
The economic model of traditional SIEMs creates what industry analysts describe as a “security tax”—the cumulative cost burden of licensing, infrastructure, and data management that constrains visibility into security events. Organizations must carefully curate which log sources to ingest, which events to index, and how long to retain data, effectively creating artificial blindspots in their security posture. This approach stems from the infrastructure limitations of earlier generations of database technology, where storing and querying large-scale security telemetry proved prohibitively expensive.
Key limitations include:
Lakehouse platforms combine data lake flexibility with data warehouse query capabilities, utilizing open standards and commodity cloud storage. Lakewatch applies this architecture specifically to security operations, providing what its proponents characterize as 100% telemetry visibility at petabyte scale without the cost-driven data deletion practices of traditional SIEMs 2).
The lakehouse approach leverages open table formats (such as Delta Lake or Apache Iceberg) and open standards for data storage and querying, eliminating vendor lock-in constraints. Security teams can ingest the complete telemetry stream from all sources—endpoint agents, network sensors, cloud API logs, application events, and third-party security tools—and retain this data for extended periods at significantly lower cost than traditional SIEM infrastructure.
Key architectural differences include:
The shift from selective data retention to comprehensive telemetry preservation fundamentally changes security operations. With access to complete historical data, security teams can perform retroactive threat investigation without time or completeness constraints. When a threat is discovered, analysts can query back through months or years of comprehensive telemetry to identify initial compromise indicators, lateral movement patterns, and data exfiltration pathways 3).
Lakewatch additionally enables unified data analysis by consolidating security telemetry with business context, user behavior analytics, and infrastructure metrics in a single queryable platform. This integration supports more sophisticated threat detection strategies that correlate security signals with operational data, improving detection precision and reducing false positive rates.
The architecture also facilitates agentic threat response—autonomous or semi-autonomous systems that can automatically investigate alerts, gather supporting evidence, execute containment actions, and correlate findings across the complete telemetry dataset. Traditional SIEMs support alert-driven automation, but limited by their proprietary APIs and constrained data models; lakehouse architectures enable more sophisticated reasoning over complete datasets.
Traditional SIEM costs typically scale linearly with data volume ingested, creating economic pressure to reduce telemetry collection as organizations grow. Cloud storage for lakehouse platforms scales differently, with commodity storage costs declining per gigabyte as scale increases. This inverted economics means that large organizations with massive telemetry volumes benefit proportionally more from lakehouse approaches.
However, lakehouse approaches introduce distinct operational considerations. Organizations must develop competency with open query languages and data management practices. The flexibility of open standards requires more operational discipline to maintain data quality, schema consistency, and performance optimization compared to the prescribed data models of traditional SIEMs.