NC 01: Discovery, tables and schema

Track: No-code sandbox (sandbox.altfndata.com) Prerequisite: A sandbox account, self-registered with a work or school email (auto-approved, no API key needed).

The task

Before writing a single query, find out what tables exist in the ALT/FNDATA dataset and what columns live inside one of them, so you know what you are working with.

Starter steps

  1. Log in to sandbox.altfndata.com and open the workspace.
  2. Look for the Coverage or Tables panel in the left sidebar. This lists every table available to you, for example all_watches_data, all_jewels_gems_data, all_handbags_data, and all_fine_art_data, alongside dozens of others covering categories from wine and whisky to automobiles to books.
  3. Click the Schema tab (sometimes shown as "Data dictionary") next to any table name, for example all_watches_data. This opens a column-by-column list: field name, data type, and a short description.
  4. Locate these fields in the schema, since you will use them throughout this track: designer, model, item_title, sale_date, usd_price_decimal, sale_estimates_high_usd_price, status, vendor, stock_ticker.
  5. In the SQL editor, run a lightweight discovery query to confirm the table is live and see a few raw rows:
SELECT designer, model, item_title, sale_date, usd_price_decimal, status
FROM all_watches_data
LIMIT 5;

Expected result

The Coverage panel shows a list of table names, each with a row count and a coverage summary (date range, vendor count). The Schema tab shows a readable table of columns for all_watches_data, including the documented fields above. The SQL query returns a small result grid of five rows, mixing sold and unsold lots, with populated designer and model names and a sale_date in a standard date format.

Stretch challenge

Open the schema tab for two other tables, all_jewels_gems_data and all_handbags_data, and compare their column lists to all_watches_data. Note which documented fields (designer, item_title, sale_date, usd_price_decimal, status, vendor, stock_ticker) are shared across all three, since a shared field vocabulary is what lets you write similar queries across categories.