Google Sheets or BigQuery? Choosing the Right Source for a Data Studio Report

• Where Google Sheets is the right choice
• Signs a Sheet has outgrown the job
• What BigQuery changes
• A middle path I use often
• How I decide on a project

Almost every client who comes to me with a growing Data Studio (formerly Looker Studio) report eventually asks the same question: should this stay in Google Sheets, or is it time to move to BigQuery? I’m Harsh Khubchandani, an independent Data Studio consultant in New Delhi, and this is the way I think it through. There is no rule that one is better. The right answer depends on the data, the people and the budget.

§Where Google Sheets is the right choice

Sheets is a perfectly good source for a lot of business reporting. I keep a report on Sheets when:

• The data is small, for example monthly targets, a list of branches, or a few thousand rows of sales.
• People need to type or paste values by hand, such as forecasts, budgets or manual adjustments.
• The team already knows Sheets and nobody wants to learn SQL.
• The report is read by a handful of people, a few times a week.

Sheets also works well as a lookup or mapping table that is blended with a bigger source, like a campaign-to-product mapping the marketing team maintains.

§Signs a Sheet has outgrown the job

• Charts take a long time to load, or show errors when several people open the report at once.
• The Sheet is assembled from many tabs with formulas that break when someone inserts a column.
• Different people paste exports in slightly different formats, and totals drift.
• You need history that goes back years, row by row, not just a summary.
• You want to join GA4, ad platform and CRM data properly rather than blending in the report.

When I see two or three of these together, a database is usually worth the effort.

§What BigQuery changes

BigQuery lets me store raw data once, clean it with SQL, and give Data Studio a small, tidy table to read. The report gets faster and the logic lives in one place instead of being scattered across calculated fields. GA4 can export event data to BigQuery directly, which removes many of the sampling and quota issues people run into with the GA4 connector.

The trade-offs are real, though:

1. Someone has to own the SQL. If the client has no one who can read a query, I document every table and keep the logic simple.
2. BigQuery bills for storage and queries. For most small and mid-sized reports the cost is modest, but a badly designed report can scan far more data than needed. I set up partitioned tables and summary tables so dashboards don’t scan raw events on every page load.
3. Manual inputs don’t belong in BigQuery. I usually keep targets and mappings in Sheets and connect them to BigQuery as an external table or a scheduled load.

§A middle path I use often

For many clients the best setup is both: raw and high-volume data in BigQuery, small human-maintained tables in Sheets, and Data Studio reading one clean, pre-joined table. That keeps the report fast while letting the business keep editing the bits that change by hand.

§How I decide on a project

I look at row counts, how often the data changes, who maintains it, and what the report needs to answer next year, not just today. Then I give a fixed price for whichever option fits, including documentation the team can follow without me.

If you’re unsure whether your report should move off Sheets, I offer a free 30-minute review. More details at datastudioconsultant.com.

Harsh Khubchandani, Data Studio Consultant, New Delhi, India