Use Connected Sheets list parameters to filter BigQuery with a cell range

Connected Sheets list parameters let a BigQuery query take multiple filter values from a Google Sheets range. Put one value in each cell, map that range to a named parameter, and use the parameter in GoogleSQL.

Google announced list parameters on August 11, 2026, with rollout expected to finish by August 15. Each cell in the selected range becomes a distinct list value. This means an analyst can maintain the SQL while collaborators change filter inputs—such as dropdown selections—without editing the query (Google Workspace Updates).

Build the input range

Create a small input area with one filter value per row. This is a sample structure, not live data:

A	B
Region filter	Notes
North	Included
South	Included
West	Included

Use A2:A4 as the parameter range; do not include the header or notes. For a maintainable input sheet:

• select only the cells intended to supply values;
• use values compatible with the BigQuery field being filtered;
• use dropdowns when collaborators should choose from an approved vocabulary;
• keep instructions and notes outside the parameter range.

Do not put North, South, West in one cell when you want each region treated as a separate list value.

Connect the range to a parameter

You need BigQuery access and a Google Cloud project with billing configured. Connected Sheets runs BigQuery queries manually or on a schedule and saves their results in the spreadsheet. It cannot change the underlying BigQuery data (Google Docs Editors Help).

On a computer:

1. Open the spreadsheet in Google Sheets.
2. Choose Data > Data connectors > Connect to BigQuery.
3. Select the project, then choose Saved queries and query editor.
4. In the panel on the right, choose Parameters > Add.
5. Enter REGIONS as the parameter name, without @.
6. Choose Cell Reference.
7. Select or enter Inputs!A2:A4, click OK, and then click Add.
8. Enter the query. You can choose Preview results to check how much data it scans.
9. Click Connect when the query is ready.

Google’s current query instructions allow a parameter’s cell reference to contain one or more cells and recommend UNNEST() for a list parameter (Google Docs Editors Help).

Use the list in GoogleSQL

For a BigQuery table with a region column, use this pattern:

SELECT
  region,
  product_name,
  report_date,
  amount
FROM `project_id.dataset_id.sales`
WHERE region IN UNNEST(@REGIONS)
ORDER BY report_date DESC, region, product_name;

Replace the project, dataset and table identifiers with your own. The parameter is named REGIONS in the setup panel and referenced as @REGIONS in the query.

The result contains records whose region matches any value in Inputs!A2:A4. If a collaborator replaces West with East, the SQL stays the same. An authorized user must then refresh the result to run the query with the revised list.

This is BigQuery’s documented array-parameter pattern: named parameters use an @ prefix in SQL, and an array can be tested with IN UNNEST(@parameter) (BigQuery documentation). Parameters supply values only; they cannot replace table names, column names or other identifiers.

When the filter is stored in one cell

Google’s query help also documents a single-cell, comma-separated pattern:

WHERE region IN (
  SELECT TRIM(value)
  FROM UNNEST(SPLIT(@REGIONS_TEXT, ',')) AS value
)

Use that approach when the input genuinely is one text cell containing a delimited list. For several input cells, IN UNNEST(@REGIONS) avoids delimiter parsing and gives each value its own editable cell.

Refresh and prepare a publication extract

Changing the inputs does not make the result public. It changes the values available to the next query run. Filtering or refreshing Connected Sheets sends a query to BigQuery, and people who have spreadsheet access but lack BigQuery access cannot manually refresh or schedule refreshes (Google Cloud documentation). Assign refresh responsibility to someone with the required access.

Before publishing the filtered result:

1. Refresh it with an authorized account.
2. Verify the selected values, row count and date range.
3. Remove internal identifiers, personal data and operational notes.
4. Export only the reviewed result columns—not the parameter controls or unrestricted source data.
5. Publish the source, applied filters and refresh date alongside the table.

For a source too large for a normal spreadsheet import, keep the full table in BigQuery and create a documented publication extract rather than splitting rows arbitrarily. See large CSV files that exceed Google Sheets import limits for a broader workflow.