  Within enterprises, there is a growing need to intelligently navigate data
lakes, specifically focusing on data discovery. Of particular importance to
enterprises is the ability to find related tables in data repositories. These
tables can be unionable, joinable, or subsets of each other. There is a dearth
of benchmarks for these tasks in the public domain, with related work targeting
private datasets. In LakeBench, we develop multiple benchmarks for these tasks
by using the tables that are drawn from a diverse set of data sources such as
government data from CKAN, Socrata, and the European Central Bank. We compare
the performance of 4 publicly available tabular foundational models on these
tasks. None of the existing models had been trained on the data discovery tasks
that we developed for this benchmark; not surprisingly, their performance shows
significant room for improvement. The results suggest that the establishment of
such benchmarks may be useful to the community to build tabular models usable
for data discovery in data lakes.
