Database Systems

Tables have a talent for pretending to be tidy. A customer table may have fifty columns. A transaction table may have a hundred. A log table may contain derived fields, timestamps, status codes, copied identifiers, normalized labels, and a few columns that nobody remembers creating but everybody is afraid to delete. Then a data profiling tool arrives, dutifully discovers functional dependencies, and returns several hundred thousand “valid” relationships. ...

TL;DR for operators Most enterprise data work is not blocked by a lack of models. It is blocked by orchestration. A company may already have Spark, Pandas, SQL engines, notebooks, dashboards, semantic layers, data lakes, vector stores, ETL jobs, monitoring tools, and a growing pile of LLM wrappers. The awkward part is deciding which tool should act, in what order, on which data, under which assumptions, and how to recover when the first plan fails. This is the gap the Data Agent paper tries to formalise.1 ...