A spreadsheet is best for flexible editing and analysis; a database is best when records need a defined structure and reliable access by applications or multiple users. Both can show data in rows and columns. Spreadsheet apps also have capacity limits: check the Apple Numbers row limit if you plan to open a large table there. The practical difference is how they store, validate, relate, and query those records.
In a spreadsheet, you can add a column, write a formula, sort rows, and explore a small dataset without designing a schema first. Excel and Google Sheets also offer data-validation rules, so it is inaccurate to say they have no structure at all. Those rules are often optional or easier to bypass than constraints enforced by a database.
A database defines fields and their types in a schema. It can enforce requirements such as a unique customer ID and connect customers to orders through that ID. Applications can query and update those records through controlled operations. This makes a database a better fit when several systems must rely on the same data, though it takes more setup and maintenance.
For example, a sales team might use a sheet to review a one-off CSV of 200 leads. If its CRM and billing system must share customer records, a database with a unique customer ID and defined relationships is a safer source of truth. Datablist sits between these approaches: a collection has spreadsheet-like editing plus typed properties and unique-value rules for list workflows. See when to use a spreadsheet or a database for the decision criteria.