If you already think in Excel formulas, the fastest way to learn SQL with Excel data is to use your own sheets instead of working through generic tutorial tables you don't care about. This walks through importing a spreadsheet, running real queries against it, and exporting the results back out — using FoxSchema's SQL Editor the whole way.
Step 1 — get your sheet into CSV
FoxSchema's Query files utility imports CSV/TSV, JSON, and fixed-width text directly — it doesn't read .xlsx workbooks natively yet. The practical workaround costs one click: in Excel or Google Sheets, use File → Save As / Download → CSV for the sheet you want to query. If your workbook has several sheets you care about, export each one as its own CSV — you'll import them as separate tables and can join them in SQL, which is the whole point of doing this in a database instead of VLOOKUP.
A typical export might look like this orders.csv:
order_id,customer,product,quantity,price,order_date
1001,Acme Co,Widget,4,12.50,2026-01-05
1002,Acme Co,Gadget,1,89.00,2026-01-06
1003,Beta LLC,Widget,10,12.50,2026-01-09
1004,Beta LLC,Sprocket,2,45.00,2026-01-12Step 2 — import it
Open the SQL Editor, then Utilities → Query files. Pick your CSV, confirm the delimiter (comma is the default), and leave the destination on New temp SQLite workspace — it's the fastest way to start and needs no server setup. FoxSchema creates a Files: … connection and a table matching your columns, checked automatically as a Destination so you can query it immediately.
Step 3 — learn SQL with your Excel data: first queries
Against the orders table above, the SQL equivalents of common spreadsheet tasks:
-- SUM(quantity*price), grouped — like a pivot table
SELECT product, SUM(quantity * price) AS revenue
FROM orders
GROUP BY product
ORDER BY revenue DESC;
-- filter, like AutoFilter
SELECT * FROM orders
WHERE order_date >= '2026-01-06'
ORDER BY order_date;
-- top N, like sorting + trimming rows
SELECT * FROM orders
ORDER BY quantity DESC
LIMIT 3;The schema explorer on the left lists your imported table and its columns — click a column name to insert it at the cursor instead of retyping it, and autocomplete will suggest it as you type.
Step 4 — bring in a second sheet and join them
Export a related sheet as its own CSV, e.g. customers.csv:
customer,region
Acme Co,West
Beta LLC,EastImport it with Add table to existing Files workspace so it lands as a second table alongside orders in the same temp database. Now a join works exactly like it would against a real database:
SELECT c.region, SUM(o.quantity * o.price) AS revenue
FROM orders o
JOIN customers c ON c.customer = o.customer
GROUP BY c.region
ORDER BY revenue DESC;This is the exercise that VLOOKUP and pivot tables approximate but never quite make explicit: matching rows across two tables on a shared key, then aggregating the result.
Step 5 — export the result back to a spreadsheet
Every result grid has an Export CSV button in its toolbar. Run your query, click it, and the query result downloads as a CSV you can reopen directly in Excel or Google Sheets — useful for sharing a query's output with someone who isn't going to open the SQL Editor themselves, or for feeding the result into a chart back in your spreadsheet tool.
Where to go next to learn SQL with Excel data
Once a query against your own data clicks, the natural next steps are variables (reuse a value across several queries with ${{name}}), multi-destination Run (compare the same query against several imported files or real connections at once), and eventually pointing the same SQL Editor at a real database instead of a CSV. See the Query files guide for the full import reference, or install FoxSchema and try this against your own spreadsheet.
Further reading: for more on learn SQL with Excel data, see the CSV format overview and the SQLite documentation.
