Google BigQuery Studio: A Complete Interface Overview

Google BigQuery Studio is the browser-based interface where you write SQL queries, explore your datasets, and view your results. You access it through the Google Cloud console at console.cloud.google.com/bigquery.

You do not install any software. Everything runs in the browser.

The Three Main Panels

BigQuery Studio divides the interface into three panels.

• Left panel: Explorer. This is where you navigate your projects, datasets, and tables. You see your entire BigQuery data structure in a tree view. You expand a project to see its datasets. You expand a dataset to see its tables. Clicking on a table shows you its schema, the column names, data types, and descriptions.
• Center panel: Query editor. This is where you write SQL queries. You type your query in the editor and click “Run” to execute it. The editor has basic autocomplete for table names and column names.
• Bottom panel: Results. After you run a query, the results appear here as a table. You see the rows and columns returned by your query. You can download the results as a CSV or save them to a new BigQuery table.

You can collapse the left panel to give yourself more space in the query editor. The toggle button is at the top left of the interface.

The Top Navigation Bar

At the top of BigQuery Studio, you always see the project you are currently working in. If you manage multiple projects, you click the project name to switch between them.

The top bar also shows:

A search bar for finding tables, datasets, and queries across your projects.

A Gemini button for AI-assisted query writing (available in some regions).

A notification bell for alerts about your BigQuery usage and jobs.

A help button for documentation and support.

The Explorer Panel in Detail

The Explorer panel on the left is your navigation tool. It has two tabs.

The first tab shows your pinned projects. You pin the projects you use most often so they appear at the top. Click the three-dot menu next to any project name to pin it.

The second tab shows recent resources, including tables and queries you have accessed recently.

When you find a table in the Explorer, clicking on it opens a new tab in the center panel with three sub-tabs: Schema, Details, and Preview.

• Schema: shows every column in the table, its data type (STRING, INTEGER, FLOAT, DATE, TIMESTAMP), and its mode (NULLABLE or REQUIRED).
• Details: shows metadata about the table, including its size in bytes and the number of rows.
• Preview: shows the first 100 rows of the table without running a query. This is useful for quickly checking what your data looks like.

Writing and Running Queries

To write a query, click the “+” button at the top of the center panel to open a new query tab. You write standard SQL in the editor. BigQuery uses a SQL dialect called GoogleSQL, which is compatible with standard SQL.

A basic query to see all your data from a table looks like this:

SELECT * FROM your_dataset.your_table LIMIT 100

The LIMIT 100 at the end is important. Without it, BigQuery scans the entire table, which increases the data processed and potentially the cost.

After you type your query, the top right of the editor shows you how much data the query will process. This estimate updates as you type. Use it to check the cost of your query before running it.

Click “Run” to execute. Results appear in the bottom panel within seconds for most marketing datasets.

Saving and Sharing Queries

BigQuery Studio lets you save queries. Click the “Save” button above the editor to save your query to your project. Saved queries appear in the Explorer panel under “Saved queries.”

You share saved queries with other users who have access to your project. This is useful for building a library of standard marketing queries that your team reuses, such as a daily spend summary or a cross-channel ROAS calculation.

Loading Your Marketing Data Into BigQuery Studio

Before you can query your marketing data in BigQuery Studio, you need to load it into BigQuery. Porter Metrics connects your ad platforms, GA4, and CRM to BigQuery automatically. Once your data is loaded, it appears in the Explorer panel as tables inside your datasets, ready to query.