Tabular Data is structured data that is organized in a table (grid) of rows and columns, much like data in a spreadsheet or SQL database table​.Each row in tabular data represents an individual record or example (also called an instance), and each column represents a feature/attribute of those records. For example, a table of customer information might have each row as a customer and columns for attributes like age, gender, and purchase history. Tabular datasets are common in business applications (think of CSV files or Excel sheets) and are the typical input for many classical machine learning algorithms (like linear models, tree-based models, etc.). Because the data is structured, one can perform operations like sorting by a column, filtering rows by some criteria, and aggregating values by categories. In machine learning contexts, handling tabular data may involve feature engineering on the columns (scaling numeric features, encoding categorical features, etc.). Tabular data is contrasted with unstructured data types such as free text, images, or audio. Most AutoML and traditional ML workflows predominantly deal with tabular data, where the schema (column definitions) is known and each feature can be leveraged by the model directly.