  In an era of rapidly advancing data-driven applications, there is a growing
demand for data in both research and practice. Synthetic data have emerged as
an alternative when no real data is available (e.g., due to privacy
regulations). Synthesizing tabular data presents unique and complex challenges,
especially handling (i) missing values, (ii) dataset imbalance, (iii) diverse
column types, and (iv) complex data distributions, as well as preserving (i)
column correlations, (ii) temporal dependencies, and (iii) integrity
constraints (e.g., functional dependencies) present in the original dataset.
While substantial progress has been made recently in the context of
generational models, there is no one-size-fits-all solution for tabular data
today, and choosing the right tool for a given task is therefore no trivial
task. In this paper, we survey the state of the art in Tabular Data Synthesis
(TDS), examine the needs of users by defining a set of functional and
non-functional requirements, and compile the challenges associated with meeting
those needs. In addition, we evaluate the reported performance of 36 popular
research TDS tools about these requirements and develop a decision guide to
help users find suitable TDS tools for their applications. The resulting
decision guide also identifies significant research gaps.
