Uploading & ingesting data

There are two ways to get records into an artifact source: upload a file, or send data through the API.

Admire is built to make this trivial. When records arrive, it detects the columns automatically and infers each column’s type (text, number, date, true/false, and so on), widening a type later if new data needs it. You don’t define a schema by hand. The point is to get data in, connect it to your people, and visualize it for coaching with as little setup as possible.

Core concepts

A source has one column that matters for loading: the ref_id, the identifier each record carries from its origin system (shown as Reference ID in the interface). Everything else is flexible.

• ref_id: the stable, unique key for a record within the source. It powers deduplication (below), so the same record updates in place instead of piling up duplicates on re-upload. In a CSV, name this column either ref_id or Reference ID; both are recognized and behave identically.
• Every other column is optional and can be any type. You don’t pre-declare them; Admire creates and types columns from the data as it arrives. There’s no required timestamp, owner, or status column. Include whatever your data has.
• From JSON, any path can become a column. When you ingest JSON, a column can route to any field, including a nested one (for example $.author.login), so any part of a payload becomes a tidy, typed column. See arbitrary JSON rows.

Deduplication

Admire deduplicates on the ref_id within a source: re-sending a record with a ref_id it has already seen updates the existing row instead of creating a duplicate. That’s what makes repeated syncs, and re-uploading a corrected file, safe to run as often as you like. (If a record arrives without a ref_id, Admire still loads it, but it can’t recognize it as the same record later, so provide one whenever you want re-uploads to stay idempotent.)

CSV upload

Upload a CSV directly. Admire reads the header row, adds any new columns to the source automatically, and infers each one’s type, so you can start from a spreadsheet without defining the schema by hand. Include a Reference ID (or ref_id) column to get dedup-safe re-uploads.

A simple file might look like this:

Reference ID	Assignee Email	Created At	Priority	Status	Resolution Minutes
TKT-10231	maya.chen@acme.com	2026-05-02	high	closed	124
TKT-10232	david.okafor@acme.com	2026-05-02	normal	closed	47
TKT-10233	maya.chen@acme.com	2026-05-03	low	open

Here Reference ID becomes the dedup key, Created At is detected as a date, Resolution Minutes as a number, and the rest as text. Blank cells are simply left empty.

Headers become stable references

Admire keeps your original header as the column’s display name, but derives a stable reference from it for querying and metrics. The reference is the header lowercased, with spaces and punctuation turned into underscores, so Assignee Email becomes assignee_email and Created At becomes created_at, while the columns still read as “Assignee Email” and “Created At” in the interface. The deduplication column works the same way: Reference ID and ref_id are interchangeable headers for the same ref_id key. When you query your data, reference the stable name.

API ingestion

For ongoing or automated feeds, send records through the API, up to 500 records per call. This is the route for syncing data from another system on a schedule. Each record carries its own ref_id, so scheduled syncs keep rows up to date rather than duplicating them. Over the API, supply ref_id directly as a field on each record (the Reference ID header is a convenience for CSV uploads), and key the rest of the record by each column’s stable reference.

Next

With data loaded, query it to power reports and metrics.