Google Sheets vs Looker Studio vs BigQuery: Which Should You Use?

Google Sheets, Looker Studio, and Google BigQuery all help marketing teams work with data. But they are not interchangeable. Each tool is built for a different scale and a different type of work. Here is how to choose the right one for your situation.

Google Sheets: Where Every Marketing Team Starts

Google Sheets is the starting point for most marketing teams. It is familiar, flexible, and requires no setup.

Use Google Sheets when:

Your dataset has fewer than 100,000 rows. Sheets handles small data well, but it slows down significantly beyond that.

You are doing ad hoc analysis that does not need to repeat on a schedule.

Your team does not know SQL and needs to work with formulas and pivot tables.

You are sharing data with stakeholders who are comfortable in spreadsheets.

The advantages of Google Sheets are real. No SQL required, no infrastructure to set up, easy to share, and familiar to almost everyone. You can pull data from Google Ads and Google Analytics directly into Sheets using native connectors.

The limits are also real. Sheets breaks with large datasets. It does not handle multiple data sources well. And every analysis is manual, meaning someone has to update the data and rebuild the report each time.

Looker Studio: The Free Reporting Layer

Looker Studio is Google’s free dashboarding tool. It connects to dozens of data sources and lets you build visual reports without writing SQL.

Use Looker Studio when:

You want to share visual dashboards with clients or stakeholders.

Your data comes from a small number of sources (one to three platforms).

You need a report that updates automatically without manual work.

Your data volume is manageable and your queries are simple.

Looker Studio is excellent for standard marketing dashboards. A Meta Ads report, a Google Ads report, or a combined paid media overview all work well in Looker Studio when connected directly to the platform APIs.

The limits appear when you need to blend data from many sources, apply custom logic, or work with large historical datasets. Looker Studio connected directly to ad platform APIs also loads slowly, often taking 20 to 30 seconds per page, because it queries the live API every time someone opens the report.

Google BigQuery: For Scale, Complexity, and Speed

BigQuery is the right choice when your data outgrows Sheets and your reporting requirements go beyond what Looker Studio can handle with direct API connections.

Use BigQuery when:

Your dataset exceeds 100,000 rows or you are storing multiple months or years of data.

You need to combine data from more than three sources in a single report.

You want dashboards that load in 2 to 3 seconds instead of 20 to 30 seconds.

You need custom metrics that require SQL calculations.

You want to store historical data beyond what your ad platforms retain.

You manage multiple clients and need a unified data layer across all accounts.

The main requirement for BigQuery is SQL. You need to write queries to extract and transform your data. For teams without SQL experience, this is a learning curve. But for teams that invest in it, BigQuery removes most of the limitations that Sheets and Looker Studio impose.

How the Three Tools Work Together

In practice, most mature marketing teams use all three tools in combination.

BigQuery is the data layer. All raw marketing data from ad platforms, CRM, and analytics flows into BigQuery via a connector like Porter Metrics. BigQuery stores it, cleans it, and makes it queryable.

Looker Studio is the reporting layer. It connects to BigQuery instead of directly to the ad platform APIs. Because the data is already processed and stored in BigQuery, Looker Studio reports load in seconds. Stakeholders see fresh, accurate data every time they open a dashboard.

Google Sheets is the analysis layer. When someone needs to do a one-off analysis, export a specific dataset, or share a table with a client, they pull data from BigQuery into Sheets using a BigQuery connector.

Decision Framework: Which Tool Should You Use?

Here is a simple way to decide:

If your data is under 100,000 rows and your analysis is one-time: use Google Sheets.

If you need visual dashboards from one to three sources and your data is current: use Looker Studio with direct API connections.

If you need to combine multiple sources, store historical data, build fast dashboards, or apply custom metrics: use BigQuery as your data layer, with Looker Studio on top.

Most marketing teams start with Sheets, move to Looker Studio as they grow, and add BigQuery when their data volume and reporting complexity require it.

Moving Your Marketing Data to BigQuery

Porter Metrics connects your marketing platforms to BigQuery automatically. You connect your ad accounts, select your data, and Porter loads everything into BigQuery on a daily schedule. From there, you connect Looker Studio to BigQuery and your dashboards load in seconds with fresh, centralized data.