Profiling Tables

This is the kind of profiling workflow you'd normally build yourself after the third time someone asks "what's actually in this table?" It runs through the standard checklist: column metadata, row counts, type-specific statistics (min/max/avg for numbers, length distributions for strings, date ranges for timestamps), cardinality analysis to spot IDs versus categories, and a data quality assessment covering completeness, uniqueness, and freshness. The output is a structured summary with a schema table, quality scores out of 10, and a list of potential issues. It's thorough without being clever, which is exactly what you want when onboarding someone to an unfamiliar dataset or debugging why a pipeline is producing garbage.