Explore BigQuery in the Google Cloud console

The BigQuery Google Cloud console provides a graphical interface that you can use to create and manage BigQuery resources. You can also use the console to complete tasks such as running SQL queries and creating pipelines.

In this walkthrough, you explore the components of the BigQuery Google Cloud console.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

1. Enable the BigQuery API.

   Roles required to enable APIs

   To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

   Enable the API

   For new projects, the BigQuery API is automatically enabled.

2. Optional: Enable billing for the project. If you don't want to enable billing or provide a credit card, the steps in this document still work. BigQuery provides you a sandbox to perform the steps. For more information, see Enable the BigQuery sandbox.
   Note: If your project has a billing account and you want to use the BigQuery sandbox, then disable billing for your project.

Open the Google Cloud console

1. Go to the Google Cloud console.

   Go to the console

2. In the Google Cloud console toolbar, click Navigation menu.

3. Click Solutions > All products.

4. In the Analytics section, click BigQuery.

   The BigQuery Studio page opens.

5. To expand or collapse the menu, click or Toggle BigQuery navigation menu.

   

You can use the navigation menu to open the following pages:

• Overview (Preview): lets you discover tutorials, features, and resources.
• Studio: lets you display your BigQuery resources and perform common tasks.
• Search (Preview): lets you search for Google Cloud resources from BigQuery by using natural language queries.
• Agents (Preview): lets you create and chat with data agents that are designed to answer questions about BigQuery resources.

You can also use the navigation menu to perform specific tasks in the following menu sections:

• Pipelines and integration: lets you create and configure data transfers, create and list Dataform repositories, and create and list scheduled resources such as scheduled queries.
• Governance: lets you display shared data exchanges and cleanrooms, view policy tags, and curate metadata.
• Administration: lets you perform administrative tasks such as monitoring, viewing information about jobs, managing capacity, viewing information about disaster recovery, and displaying recommendations.
• Migration: lets you view and set up options for migrating your data warehouse to BigQuery.
• Partner Center: provides tools and services from partners to accelerate your workflow.
• Settings (Preview): lets you customize BigQuery defaults or user interface settings.
• Release notes: contains the latest product updates and announcements for BigQuery.

The BigQuery Studio page

The BigQuery Studio page displays your BigQuery resources and lets you perform common tasks. The Studio page has the following components:

1. Explorer tab of the left pane: use the Explorer tab to work with tables, views, routines, and other BigQuery resources, and view your job history.

   The left pane also contains an option to add data to BigQuery. When you click Add data, you can use search and filtering capabilities to find a data source that you want to work with. After you select a data source, you can do the following based on the capabilities available for your data source:

   • Set up BigQuery table over external data (federation): enables BigQuery to access external data without ingesting it into BigQuery. You can create a table to access external data or create a connection to an external source.
   • Load data to BigQuery: lets you load data to BigQuery by setting up a data transfer or by using a partner capability. Loading data to BigQuery is recommended for optimal data processing at scale.
   • Change data capture to BigQuery: replicates data from a data source to BigQuery by capturing and applying changes. You can use applications such as datastream or partner solutions to ingest data from a data source.
   • Stream data to BigQuery: ingests data into BigQuery with low latency. You can use applications such as Dataflow, Pub/Sub, or partner solutions to ingest data from a data source.

   For more information about loading data into BigQuery, see Introduction to loading data.

2. Classic Explorer tab of the left pane: use the legacy version of the Explorer pane to view BigQuery resources.

3. Files tab of the left pane (Preview): use the Files tab to organize code assets such as saved queries and notebooks by using folders. For more information, see Organize code assets with folders.

4. Repository tab of the left pane (Preview): use the Repository tab to store code, edit files, and track changes using version control through repositories or by using remote Git-based repositories. For more information, see Introduction to repositories.

5. Home tab: use the Home tab to view the following resources:

   • The Check out what's new in Studio section that lists new features in BigQuery Studio. You can click Try it to view the features. If the section isn't visible, click What's new in Studio to expand the section.
   • The Create new section that has options to create a new SQL query, notebook, Apache Spark notebook, data canvas, data preparation file, pipeline, or table.
   • The Recent section where you can view your 10 most recently accessed resources. These resources include tables, saved queries, models, and routines.
   • The Try with templates section that lets you use templates to get started querying data and working with notebooks.
   • The Add your own data section that helps you get started loading data into BigQuery.

6. Query editor: use the query editor to create and run an interactive query. You can also view the results in the Query results pane that opens after you run the query.

Explore the Studio page

The Studio page BigQuery is the central point for viewing your BigQuery resources and for performing common tasks such as creating datasets and creating and running notebooks.

Note: To see how to use keyboard shortcuts in Studio, click BigQuery Studio shortcuts in the BigQuery Studio toolbar:

To explore the Studio page, follow these steps:

1. In the Google Cloud console, go to the BigQuery Studio page.

   Go to Studio

   Alternatively, enter the following URL in your browser:

   https://console.cloud.google.com/bigquery

   The Studio page opens in your most recently accessed project.

2. In the left pane, click Explorer.

   The Explorer pane lists different code assets and data resources, and it lets you search for BigQuery resources.

   Note: You can expand and collapse the left pane by clicking Collapse left pane or Expand left pane.

3. Go to the bigquery-public-data project, click Toggle node to expand it, and then click Datasets. A new tab opens in the details pane that shows a list of all the datasets in the project.

   BigQuery public datasets are stored in BigQuery and made available to the general public through the Google Cloud Public Dataset Program.

4. In the list, click the austin_crime dataset.

5. On the Overview tab, view the resources stored in the dataset such as tables, models, and routines.

6. Click the Details tab. This tab shows all details for the dataset including metadata information.

7. To navigate different tabs and resources, use the breadcrumb trail as shown in the following example:

   

8. In the Explorer pane, click Job history. This opens the list of job histories in a new tab:

   

   Every time you load, export, query, or copy data, BigQuery automatically creates, schedules, and runs a job that tracks the progress of the task.

   1. To view details of your own jobs, click Personal history.

   2. To view details of recent jobs in your project, click Project history.

      Note: To see the details of a job or to open a query from a query job, in the Actions column for a job or query, click Actions > Show job details or View job in editor.

9. In the left pane, click the folder_data Repository tab (Preview).

   You can use repositories to perform version control on files that you use in BigQuery. BigQuery uses Git to record changes and manage file versions.

   You can use workspaces within repositories to edit the code stored in the repository. When you click a workspace in the Git repository pane, it opens in a tab in the details pane.

10. In the left pane, click Files (Preview).

    The Files tab lets you create user and team folders that store and organize your code assets.

11. Click the Home tab.

    The Home tab provides links and templates that let you get started using BigQuery.

    If you close the Home tab, you can open it by clicking Home in the Explorer tab.

12. Click the query editor. This tab is labeled search_insights Untitled query.

    You use the query editor to create SQL queries, run SQL queries, and view the results.

    If you close the query editor, you can open it by clicking the Home tab, and then in the Create new section, click SQL query.

Work with tabs in Studio

Whenever you select a resource or click SQL query in the details pane, a new tab opens. If more than one tab is open, you can split the tabs into two panes and view them side by side.

Prevent tabs from being replaced

To reduce tab proliferation, clicking a resource opens it within the same tab. To open the resource in a separate tab follow these steps:

1. Press Ctrl (or Command on macOS) and click the resource.

2. Alternatively, double-click the tab name. The name changes from italicized to regular font.

3. If you accidentally replace the current page, you can locate it by clicking tab_recent Recent tabs in the details pane.

Split and unsplit tabs

To split tabs into two panes, follow these steps:

1. Next to the tab name, click Open menu.

2. Select one of the following options:

   • To place the selected tab in the left pane, select Split tab to left.

   • To place the selected tab in the right pane, select Split tab to right.

   Note: If only one tab is open, these menu options are unavailable.

3. To unsplit the tabs, select Open menu on one of the open tabs, and then select Move tab to left pane or Move tab to right pane.

Query data using split tabs

To split tabs when querying tables, follow these steps:

1. In the Explorer menu, click the table that you want to query.

2. Click Query, and then click In new tab or In split tab:

   

3. Click the field name that you want to query:

   

The following image shows the details pane with two open tabs. One tab has a SQL query, and the other tab shows details about a table.

Move tabs between panes

To move a tab from one pane to the other pane, follow these steps:

1. Next to the tab name, click Open menu.

2. Select Move tab to right pane or Move tab to left pane (whichever option is available).

Close all other tabs

To close all tabs except for one, follow these steps:

1. Next to the tab name, click Open menu.

2. Select Close other tabs.

The Overview page

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Note: To provide feedback on the Overview page, click Help > Send feedback.

The BigQuery Overview page is your hub for discovering tutorials, features, and resources to help you get the most out of BigQuery. It provides guided paths for users of all skill levels, whether you are running your first query or exploring advanced AI/ML capabilities.

You can use the Overview page to find resources organized by your role or interest like data analysis or data science. These resources let you find the most relevant content to get started quickly.

Explore the Overview page

1. In the console, go to the Overview page.

   Go to Overview

   You can also open the BigQuery Overview page by entering the following URL in your browser:

   https://console.cloud.google.com/bigquery/overview

2. Review the following sections of the Overview page:

   • The Introduction section: gives you a quick video overview of BigQuery's capabilities.

   • The Get started section: designed for learning by doing. Here you can launch interactive guides that help you learn how to use BigQuery features.

   • The Find out more section: shows the BigQuery release notes so you can view the latest feature announcements and updates.

   • The Explore possibilities section: provides in-depth tutorials and learning opportunities for specific features.

Customize the Overview page

You can customize the Overview page to show or hide information relevant to your task or role.

1. On the Overview page, go to the filter bar.

   

2. Click the option that best matches your current task or role:

   • Data analysis
   • Data science
   • Data engineering
   • Data administration

   Selecting a task dynamically changes the content in the Introduction, Get started, and Explore Possibilities sections to show the most relevant content.

3. Optional: To tailor the content on the Overview page to your specific needs, hide individual cards:

   1. In the card, click More options.

   2. Choose Hide card. Your preferences for hidden cards are saved per user.

   3. To unhide the card, at the end of the section, click Show hidden content.

4. If an entire section is not relevant, click to collapse it. Your user preferences for collapsed sections are saved.

The Search page

The Search page (Preview) lets you search for Google Cloud resources from BigQuery by using natural language queries.

For information about opting into using the Search page, see Search for resources.

The Agents page

The Agents page (Preview) is a central location for creating and chatting with data agents that are designed to answer questions about BigQuery resources.

Data agents contain table metadata and use case-specific query processing instructions that define the best way to answer user questions about a set of tables that you select. Users can have conversations with data agents to ask questions about BigQuery data using natural language. For more information, see Create data agents.

For information on creating agents and using conversational analytics, see Conversational analytics in BigQuery.

What's next

• To learn about querying a public dataset and using the BigQuery sandbox, see Try BigQuery using the sandbox.
• To learn how to load and query data in the Google Cloud console, see Load and query data.