There are two ways to run a data lake. You can manage every table by hand — writing Airflow DAGs, scheduling Spark compaction jobs, building monitoring dashboards, and staffing a platform team that grows linearly with table count. Or you can deploy a control plane that continuously observes every table in the lake, classifies health, sequences maintenance operations, optimizes data layout for real query patterns, enforces policies at lake scale, and adapts as workloads change — while your data s...