Matano Open Source Security Data Lake vs Realm Data Haven: Side-by-Side Comparison (2026)

Security teams building detection pipelines on AWS who want to escape vendor lock-in will get the most from Matano Open Source Security Data Lake; its Detection-as-Code framework lets you write detection rules once and run them across any log source without rewrites. With 1,610 GitHub stars and a genuinely open architecture using Parquet and Apache Iceberg, you're not betting on a single vendor's data model. This is a poor fit for organizations that need a turnkey SIEM with built-in playbooks and out-of-the-box compliance dashboards; Matano is a platform you architect, not a product you adopt. SOC teams drowning in SIEM retention costs will find real relief in Realm Data Haven, which decouples long-term log storage from expensive indexing by keeping archives in cost-optimized cold storage while maintaining fast retrieval for investigations. The tool's compliance-focused retention capabilities and support for extended retention periods make it particularly strong for RS.AN incident analysis and RC.RP recovery workflows, where investigators need years of historical data without the per-gigabyte SIEM tax. Skip this if your organization needs real-time alerting or correlation across logs; Realm is purposefully a storage and retrieval layer, not a detection engine.

Based on our analysis of core features, company size fit, deployment model, here is our conclusion: