Data Engineering9 min read•February 2, 2026

BigQuery + Google Sheets: Connected Sheets on GCP

A practical guide to Connected Sheets: how to analyze BigQuery data in Sheets, key workflows, limits, refresh schedules, and best practices.

Written byLuis LapoFounder at Data Systems Academy. Focused on production data systems and ML engineering.

Why this integration matters

Connected Sheets bridges BigQuery scale with the familiarity of Google Sheets. It keeps data in BigQuery as the single source of truth while letting teams analyze billions of rows using pivots, formulas, and charts in a spreadsheet UI.

The value is simple: faster self-serve analysis without CSV exports, duplicated files, or ad-hoc SQL for every question.

What Connected Sheets actually is

Connected Sheets creates a live connection between a Google Sheet and a BigQuery table, view, or query. The sheet shows a preview of the data, but calculations run in BigQuery and return aggregated results to Sheets.

Key behaviors:

Data stays in BigQuery; Sheets consumes query results.
The connected tab is read-only; you cannot edit BigQuery rows from Sheets.
Refreshes can be manual or scheduled.

Practical use cases by role

Data analysts

Build pivots and charts on large datasets without learning SQL.
Schedule refreshes for recurring reports.
Share live summaries with stakeholders while keeping BigQuery as the source of truth.

Data engineers

Enable self-serve analysis without creating new BI dashboards.
Use Google Sheets as a data entry surface and read it as an external table in BigQuery.
Export query results to Sheets for lightweight distribution.

Developers and data scientists

Prototype apps with Apps Script or parameterized queries.
Use Sheets for quick QA or manual fixes on small samples during development.

Core workflows

1) Connect a sheet to BigQuery

In Google Sheets: Data > Data connectors > Connect to BigQuery.

Pick a project and table, or choose a public dataset. Sheets creates a connected tab with a preview (usually 500 rows) and a sidebar to build pivots, charts, or custom queries.

2) Run pivots and charts at BigQuery scale

When you build a pivot or chart, Sheets executes a query in BigQuery and returns the aggregated results. You are not limited by the 1M row limit of Sheets for analysis.

3) Use custom SQL and parameters

You can write a custom query directly in the connector UI. Parameters can be mapped to cells so users can change filters without editing SQL. Example pattern:

SELECT region, SUM(revenue)
FROM dataset.sales
WHERE order_date BETWEEN @start_date AND @end_date
GROUP BY region

4) Extract subsets for deeper inspection

The Extract feature materializes query results into a regular sheet tab. Limits apply (roughly 50k rows without cell caps, or up to 500k rows with a 5M cell limit).

5) Refresh and scheduling

Use the Refresh button for manual updates, or schedule refreshes (hourly, daily, or custom intervals). Scheduled refreshes run under the sheet owner identity and can be monitored in BigQuery job history.

What you can and cannot do

Yes

Analyze BigQuery data at scale from a spreadsheet UI.
Use pivots, charts, formulas, and custom SQL.
Share sheets with stakeholders while preserving BigQuery permissions.

Edit BigQuery rows directly from the connected tab.
Extract millions of raw rows into Sheets (limits apply).
Refresh more frequently than the scheduling limits.

Architectural patterns

Self-serve analytics BigQuery holds the warehouse; Sheets provides lightweight dashboards with scheduled refreshes.

Manual data inputs Business users edit a Google Sheet; BigQuery reads it as an external table and joins it to curated datasets.

Operational reporting Scheduled queries export curated results to Sheets for distribution, while BigQuery remains the system of record.

Best practices

Use partitioned tables and filters to reduce scan costs.
Keep connected queries focused and aggregated.
Prefer parameterized SQL to avoid duplicate sheets for each filter.
Set refresh schedules thoughtfully to avoid unnecessary BigQuery spend.
Use views for business logic and permissions, then connect Sheets to the view.

Common limits to plan for

Extract limits: roughly 50k rows without cell caps, or 500k rows with a 5M cell cap.
Pivot results: limited to around 200k rows of output.
Scheduled refresh: typically hourly or slower.
Workspace licensing required for Connected Sheets.

Conclusion

Connected Sheets is a powerful bridge between BigQuery and the everyday workflow of spreadsheets. Use it to democratize access to warehouse-scale data while keeping governance, cost, and performance under control.