Managed Iceberg Data Lakes: A Guide

Apache Iceberg has become the default table format for open data lakes. The 2025 State of the Apache Iceberg Ecosystem survey found 96.4% Spark adoption, 60.7% Trino, and growing DuckDB and Flink usage. Ryft's 2026 enterprise study reports that 58% of organizations now use Iceberg for business-critical analytics, and 79% plan to move their remaining data to it within 12 months. Adoption is no longer the question. The question is: who maintains all of this? Iceberg gives you snapshot isolation, schema evolution, hidden partitioning, and time travel. It does not give you someone to compact your files, expire your snapshots, clean up orphans, rewrite your manifests, or tell you which of your 800 tables is about to make your morning dashboards unusable. That is your job — and at scale, it is a job that breaks. This guide covers what it actually takes to run an Iceberg data lake in production: the maintenance operations, the failure modes, and how LakeOps — an autonomous control pl