How to connect BigQuery to Looker Studio and automate your reports
To connect BigQuery to Looker Studio, add a BigQuery data source to a report, choose a table or write a custom query, and decide whose credentials run it. This guide covers the steps, what every chart costs in BigQuery, how to keep a dashboard fast, and how to feed several dashboards from one owned query.
To connect BigQuery to Looker Studio, create a report, add BigQuery as a data source, pick a project, dataset and table (or paste a custom query), and click Add. The report then queries BigQuery live: every chart a viewer opens is a query in your Google Cloud project.
A note on the name: Google’s documentation now says “Looker Studio is now called Data Studio”, the name the product had until 2022. Both names are in use, and this guide says Looker Studio because that is what most people still search for.
Note: Fully rewritten in October 2026 against Google’s current documentation. It now also covers what the separate guide on connecting BigQuery with Looker Studio used to.
What you need
- A Google Cloud project with BigQuery and data in it.
- The BigQuery Data Viewer role on the data and the BigQuery Job User role on the project that will run the queries. Both are named in Google’s connect to BigQuery page, read in October 2026.
- A Looker Studio account. The standard version is free; you pay BigQuery for the queries.
Connect BigQuery to Looker Studio, step by step
Connect BigQuery to Looker Studio, step by step
- In Looker Studio, create a report, or open one and choose Add data.
- Select the BigQuery connector and allow access if asked.
- Choose where the data is:
- Recent projects or My projects: browse to a project, dataset and table.
- Shared projects: a project someone gave you access to; enter the project ID if it is not listed.
- Custom query: write SQL, and choose the billing project that pays for it.
- Public datasets: Google’s sample data, billed to a project you choose.
- Click Add.
Looker Studio reads the table’s columns as fields and you can start adding charts.
Table or custom query
Connecting a table is quickest, and it lets Looker Studio build its own queries: each chart asks only for its columns. If the table is partitioned by date, tick the option to use the partitioning column as the date range dimension, so the report’s date picker limits what BigQuery scans.
A custom query fits when the table needs shaping first: a join, a filter, a calculated column. Looker Studio wraps your query in its own for every chart, so keep it lean: name columns, filter by date, and use the date range parameters so the picker reaches inside the query. If the same SQL is going into a second report, that is the moment to stop pasting it; see the last section.
Whose credentials
The data source runs queries as someone. Google’s page lists three choices:
| Credentials | Queries run as | Use when |
|---|---|---|
| Owner’s (default) | you | viewers should see the data without BigQuery access of their own |
| Viewer’s | each viewer | access must follow each person’s BigQuery permissions |
| Service account | a service account | the report should not depend on one employee’s account |
With owner’s credentials, everything every viewer opens is billed to your project and runs with your access. A report on a personal account also stops working when that person leaves. For anything shared widely, a service account is the safer owner.
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What it costs
Looker Studio is free; BigQuery is not. With on-demand pricing, each chart that loads runs a query billed by the bytes it reads. A page with ten charts, opened by twenty people, with a date picker they all play with, is a lot of queries.
Three things keep the bill small, in order of effect:
- Point the report at a small, pre-aggregated table, not at raw events. A daily summary with the dimensions the dashboard uses is usually thousands of times smaller.
- Partition by date and use the date picker so each query reads only the days shown. See partitioned tables.
- Raise the data freshness setting of the data source if the data only changes daily. Looker Studio then serves repeat views from its cache instead of asking BigQuery again.
Prices and the free tier are in BigQuery pricing. To see what a dashboard really costs, filter the job history by the report’s owner.
If the dashboard is slow
- It reads a raw table. Aggregate first; this fixes most cases.
- Blended data sources. A blend joins in Looker Studio after two queries. Do the join in BigQuery and connect the result.
- Too many charts on a page. Each is a query. Split the page.
- Calculated fields on every row. Move the calculation into SQL.
Automating the report
A Looker Studio report on BigQuery is already live: nobody presses refresh. “Automation” is about the tables underneath.
- Scheduled queries in BigQuery rebuild the summary table every morning.
- Scheduled email delivery in Looker Studio sends a PDF of the report to a list.
- The loading of the source data, such as ad platforms and the GA4 export, runs on its own schedule; see loading data into BigQuery.
When a number is wrong, the question is which of those steps ran, and in most setups the answer is spread across three screens.
One query, several dashboards
The usual end state of a team’s Looker Studio use is a dozen reports, each with its own custom query, each defining “revenue” a little differently. The chart is not the problem. The SQL lives inside the data source of each report, where nobody else can see or reuse it.
OWOX Data Marts moves that SQL out of the report. An analyst publishes the query once as a Data Mart, with a description on each column, and connects it to Looker Studio through the Data Studio destination. The report reads the Data Mart, so:
- the same definition feeds the , a Google Sheet and a scheduled email;
- a change to the SQL is made in one place;
- the query runs in your own BigQuery project, as written. OWOX Data Marts does not transform the data.
The walkthrough is in how to store SQL for Looker Studio in OWOX Data Marts. Other tools that read BigQuery are compared in BigQuery visualization tools.
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