Apache Iceberg provides the reliability layer for modern data lakehouses, but leaves table maintenance entirely to operators. At scale — hundreds of tables, multiple engines, petabytes of data — manual scripts and Airflow DAGs break down. This guide walks through the full operational stack required: query-aware compaction using a Rust/DataFusion engine, snapshot lifecycle management, manifest and metadata optimization, orphan file cleanup, organization-wide policies, multi-engine query routing (Trino, Spark, Snowflake, Athena, DuckDB), agentic AI readiness with MCP support and guardrails, and branch-based layout simulations. Each component is illustrated through LakeOps, an autonomous control plane that coordinates these operations sequentially so each step produces a cleaner input for the next, reportedly achieving 60–75% reduction in CPU and storage costs.
