  Relational tables, where each row corresponds to an entity and each column
corresponds to an attribute, have been the standard for tables in relational
databases. However, such a standard cannot be taken for granted when dealing
with tables "in the wild". Our survey of real spreadsheet-tables and web-tables
shows that over 30% of such tables do not conform to the relational standard,
for which complex table-restructuring transformations are needed before these
tables can be queried easily using SQL-based analytics tools. Unfortunately,
the required transformations are non-trivial to program, which has become a
substantial pain point for technical and non-technical users alike, as
evidenced by large numbers of forum questions in places like StackOverflow and
Excel/Power-BI/Tableau forums.
  We develop an Auto-Tables system that can automatically synthesize pipelines
with multi-step transformations (in Python or other languages), to transform
non-relational tables into standard relational forms for downstream analytics,
obviating the need for users to manually program transformations. We compile an
extensive benchmark for this new task, by collecting 244 real test cases from
user spreadsheets and online forums. Our evaluation suggests that Auto-Tables
can successfully synthesize transformations for over 70% of test cases at
interactive speeds, without requiring any input from users, making this an
effective tool for both technical and non-technical users to prepare data for
analytics.
