Open data portals contain a plethora of data files, with comma-separated value (CSV) files being particularly popular with users and businesses due to their flexible standard. However, this flexibility comes with much responsibility for data consumers, as many files contain various structural problems, e.g., a different number of cells across data rows, multiple value formats within the same column, different variants of quoted fields due to user specifications, etc. We refer to rows that contain such structural inconsistencies as ill-formed. Consequently, ingesting them into a host system, such as a database or an analytics platform, often requires prior data preparation steps.

Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is csv. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard csv formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic "pollution" process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.

Spreadsheet datasets are valuable sources of data, but often ill-suited for machine consumption. Their unstructured nature allows users to arrange data and metadata freely in a human-readable format, often in canvas-like layouts. To extract their content, data practitioners need to resort to manual inspection and run cumbersome preparation pipelines. The Mondrian system assists users in identifying and handling multiregion layout templates: spreadsheet layouts composed of independent regions that appear repeatedly across different files. Mondrian comprises an automated approach to detect multiple regions within a single file and an algorithm that leverages mapping region layouts to graphs to compute layout similarity and identify templates. Users interact with Mondrian through a web-based visual interface, that serves as a practical toolkit to handle collections of multiregion spreadsheets and enables their automated preparation.