Beyond the Forklift: Unlocking Revenue-Critical Workloads on Iceberg

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Apache Iceberg has become the open standard for modern data platforms, yet most adoptions approach migration as a forklift. Governance improves, storage is standardized, and BI workloads run reliably; but the most demanding analytics, the ones closest to revenue and customer experience, are rarely considered in scope for Iceberg.

Those SLA-bound data products—embedded dashboards, fintech merchant cash flow views, surge pricing, fraud detection, incident response—don’t simply go away when Iceberg isn’t built to serve them. If the platform isn’t engineered for deterministic p95/p99 latency and high-concurrency guarantees, the requirement resurfaces elsewhere. What was intended to be a shared system of record becomes relegated to a feeder layer, pushing data into downstream systems where it is re-stored, re-governed, and re-queried outside of Iceberg.

This session outlines a different model: Iceberg as the open system of record, paired with a purpose-built execution layer that enforces deterministic SLAs directly on Iceberg tables, eliminating shadow stacks and restoring architectural coherence for revenue-critical insights.