Datasets in GoPie

A dataset is a queryable table that lives in a project. Everything GoPie does — SQL, chat, dashboards, reports, the per-dataset REST API — runs against datasets, so this section is where most journeys start.

Know the two ways in#

• Upload a file — drop a CSV, JSON, Parquet, or Excel file on the upload wizard. Validation runs in your browser before anything uploads, and AI prepares column names and descriptions along the way.
• Connect a database or cloud source — run a SQL query against PostgreSQL, MySQL, Snowflake, or MotherDuck, or point at a file in Amazon S3, and import the result as a dataset. Source-backed datasets can be refreshed on demand or on a schedule.

Either way you end up with the same thing: a table with an AI-ready schema, a preview grid, column profiles, and its own settings.

Understand what a dataset carries#

Beyond the rows themselves, every dataset has:

• A name and description — the display name you chose, plus an editable description and an optional custom AI prompt that steer how the AI queries it.
• An internal table name — a generated identifier (shown under Dataset Details in the right rail, e.g. gp_x7k2m9qwerty) that you use in SQL. You never have to pick or manage it.
• Column descriptions and profiles — per-column documentation the AI leans on, plus computed stats like null percentages and unique counts. See Schema & column descriptions.
• Settings — description, columns, custom metadata, access, and maintenance, in a dedicated settings area. See the settings tour.
• A version history — every ingest, append, refresh, and restore records a snapshot you can preview, compare, and restore. See Version history & time travel.

Follow the lifecycle#

1. Create — upload a file or import from a source. If your project has data cleaning enabled, a Standardisation step runs before creation.
2. Explore — the dataset page opens in Preview mode: the first rows in a virtualized grid with sorting and filters, column profiles in the right rail, and row/column counts. Run SQL or ask the AI in the query section and the grid switches to your results.
3. Use it everywhere — chat with it, generate a dashboard from it, query it over its REST API, or join it with other datasets in saved views — see the Query & analyze section.
4. Keep it current — refresh from the file or the source, on demand or on a schedule.
5. Maintain — edit descriptions, manage access, prune version history, and eventually delete.

Note

Creating, editing, and deleting datasets follows your organisation role: owners and admins can create and edit datasets, members are read-only, and the person who created a dataset keeps full control of it regardless of role.

Find the right page#