  We present an in-depth analysis of data discovery in data lakes, focusing on
table augmentation for given machine learning tasks. We analyze alternative
methods used in the three main steps: retrieving joinable tables, merging
information, and predicting with the resultant table. As data lakes, the paper
uses YADL (Yet Another Data Lake) -- a novel dataset we developed as a tool for
benchmarking this data discovery task -- and Open Data US, a well-referenced
real data lake. Through systematic exploration on both lakes, our study
outlines the importance of accurately retrieving join candidates and the
efficiency of simple merging methods. We report new insights on the benefits of
existing solutions and on their limitations, aiming at guiding future research
in this space.
