# Explore BigQuery in the Google Cloud console
BigQuery in the Google Cloud console is a graphical interface
where you can create and manage your BigQuery resources. You can
use the Google Cloud console to view tables and datasets, create and run SQL
queries, build pipelines, and perform other tasks to help you manage your
BigQuery workflows.
## Before you begin
1.
Enable the BigQuery API, if it is not already enabled.
**Roles required to enable APIs**
To enable APIs, you need the `serviceusage.services.enable` permission. If you
created the project, then you likely already have this permission through the
Owner role (`roles/owner`). Otherwise, you can get this permission through the
Service Usage Admin role (`roles/serviceusage.serviceUsageAdmin`).
[Learn how to grant roles](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access).
[Enable the API](https://console.cloud.google.com/apis/enableflow?apiid=bigquery)
For new projects, the BigQuery API is
automatically enabled.
2. Optional: [Enable
billing](https://docs.cloud.google.com/billing/docs/how-to/modify-project) 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](https://docs.cloud.google.com/bigquery/docs/sandbox#setup).
> [!NOTE]
> **Note:** If your project has a billing account and you want to use the BigQuery sandbox, then [disable billing for your project](https://docs.cloud.google.com/billing/docs/how-to/modify-project#disable_billing_for_a_project).
## Open the Google Cloud console
1. Go to the Google Cloud console.
[Go to the console](https://console.cloud.google.com/)
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**](https://docs.cloud.google.com/bigquery/docs/bigquery-web-ui#open-ui) page opens.
5. To control the BigQuery navigation menu, do one of the
following:
- To expand the navigation menu, hold the pointer over the
icon, and then click
left_panel_open **Toggle BigQuery navigation menu**.
- To collapse the navigation menu, click
left_panel_close **Toggle BigQuery navigation menu**.
You can use the navigation menu to open the following pages:
- [**Overview**](https://docs.cloud.google.com/bigquery/docs/bigquery-web-ui#open-overview): lets you discover tutorials, features, and resources.
- [**Studio**](https://docs.cloud.google.com/bigquery/docs/bigquery-web-ui#open-ui): lets you display your BigQuery resources and perform common tasks.
- [**Search**](https://docs.cloud.google.com/bigquery/docs/bigquery-web-ui#search-page) ([Preview](https://cloud.google.com/products#product-launch-stages)): lets you search for Google Cloud resources from BigQuery by using natural language queries.
- [**Agents**](https://docs.cloud.google.com/bigquery/docs/bigquery-web-ui#agents-page) ([Preview](https://cloud.google.com/products#product-launch-stages)): 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](https://docs.cloud.google.com/bigquery/docs/dts-introduction), create and list [Dataform](https://docs.cloud.google.com/bigquery/docs/orchestrate-workloads#dataform) repositories, and create and list [scheduled resources](https://docs.cloud.google.com/bigquery/docs/orchestrate-workloads) such as [scheduled queries](https://docs.cloud.google.com/bigquery/docs/scheduling-queries).
- **Governance** : lets you display shared [data exchanges](https://docs.cloud.google.com/bigquery/docs/analytics-hub-manage-exchanges) and [data clean rooms](https://docs.cloud.google.com/bigquery/docs/data-clean-rooms), and manage security and access policies from the [Security center](https://docs.cloud.google.com/bigquery/docs/security-center-overview) page.
- **Administration** : lets you perform administrative tasks such as [monitoring](https://docs.cloud.google.com/bigquery/docs/admin-resource-charts), viewing information about [jobs](https://docs.cloud.google.com/bigquery/docs/admin-jobs-explorer), [managing capacity](https://docs.cloud.google.com/bigquery/docs/reservations-intro), viewing information about [disaster recovery](https://docs.cloud.google.com/bigquery/docs/managed-disaster-recovery), and displaying [recommendations](https://docs.cloud.google.com/bigquery/docs/recommendations-intro).
- **Migration** : lets you view and set up options for [migrating your data
warehouse](https://docs.cloud.google.com/bigquery/docs/migration/migration-overview) to BigQuery.
- **Partner Center** : provides tools and services from [partners](https://docs.cloud.google.com/bigquery/docs/bigquery-ready-overview#partner_center) to accelerate your workflow.
- **Settings**: lets you customize your BigQuery user and administrator settings.
- **Release notes** : contains the latest [product updates and
announcements](https://docs.cloud.google.com/bigquery/docs/release-notes) for BigQuery.
## The BigQuery Studio page
The BigQuery [**Studio**](https://docs.cloud.google.com/bigquery/docs/query-overview#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
datasets, connections, code assets, and other BigQuery
resources, and view your
[job history](https://docs.cloud.google.com/bigquery/docs/managing-jobs#list_jobs_in_a_project).
2. ***Data** tab of the left pane* : use the **Data** tab to browse and
inspect data hierarchies, including datasets, tables, views, models,
routines, property graphs, and connections, across your pinned projects.
Code assets aren't listed in the **Data** pane; instead, you can access code
assets in the **Files** tab for a single region or in the **Explorer** tab
across all regions.
The **Data** 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](https://docs.cloud.google.com/bigquery/docs/external-data-sources) or [create a
connection to an external source](https://docs.cloud.google.com/bigquery/docs/connections-api-intro).
- **Load data to BigQuery** : lets you load data to BigQuery by setting up a [data transfer](https://docs.cloud.google.com/bigquery/docs/dts-introduction) or by using a [partner capability](https://docs.cloud.google.com/bigquery/docs/load-data-third-party). 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](https://docs.cloud.google.com/datastream/docs/overview) or [partner solutions](https://docs.cloud.google.com/bigquery/docs/load-data-third-party) 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](https://docs.cloud.google.com/dataflow/docs/guides/write-to-bigquery), [Pub/Sub](https://docs.cloud.google.com/pubsub/docs/overview), or [partner solutions](https://docs.cloud.google.com/bigquery/docs/load-data-third-party) to ingest data from a data source.
For more information about loading data into BigQuery, see
[Introduction to loading data](https://docs.cloud.google.com/bigquery/docs/loading-data).
3. ***Files** tab of the left pane* :
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](https://docs.cloud.google.com/bigquery/docs/code-asset-folders).
4. ***Source control** tab of the left pane* ([Preview](https://cloud.google.com/products/#product-launch-stages)):
use the **Source control** 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](https://docs.cloud.google.com/bigquery/docs/repository-intro).
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](https://docs.cloud.google.com/bigquery/docs/running-queries#queries). You can also view the
results in the **Query results** pane that opens after you run the query.
Within the query results, the session-specific **Recent** pane lets you view
recent results.
### Explore the Studio page
The **Studio** page in 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]
> **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](https://console.cloud.google.com/bigquery)
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 the **Explorer** tab.
The **Explorer** pane lists different code assets and data resources, and it
lets you search for BigQuery resources.
> [!NOTE]
> **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](https://docs.cloud.google.com/bigquery/public-data) are stored
in BigQuery and made available to the general public through
the Google Cloud Public Dataset Program.
4. In the list, click the `america_health_rankings` 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]
> **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 fork_right
**Source control** tab ([Preview](https://cloud.google.com/products/#product-launch-stages)).
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](https://docs.cloud.google.com/bigquery/docs/workspaces) in the
**Source control** pane, it opens in a tab in the details pane.
10. In the left pane, click
**Files**.
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]
> **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
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](https://console.cloud.google.com/bigquery/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**](https://docs.cloud.google.com/bigquery/docs/search-resources) page
([Preview](https://cloud.google.com/products#product-launch-stages)) 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](https://docs.cloud.google.com/bigquery/docs/search-resources).
## The Agents page
The **Agents** page ([Preview](https://cloud.google.com/products#product-launch-stages))
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](https://docs.cloud.google.com/bigquery/docs/create-conversations)
with data agents to ask questions about BigQuery data using
natural language. For more information, see [Create data agents](https://docs.cloud.google.com/bigquery/docs/create-data-agents).
For information on creating agents and using conversational analytics, see
[Conversational analytics in BigQuery](https://docs.cloud.google.com/bigquery/docs/conversational-analytics).
## Limitations
The BigQuery Google Cloud console doesn't support
[Virtual Private Cloud](https://docs.cloud.google.com/vpc-service-controls/docs/supported-products#console) or
[Private Service Connect](https://docs.cloud.google.com/vpc/docs/private-service-connect).
## Troubleshoot BigQuery Google Cloud console issues
If you experience issues in the Google Cloud console when using
BigQuery, such as pages not loading, slow responsiveness, or
missing features, try the following troubleshooting steps.
### Common browser fixes
Many Google Cloud console issues are caused by local browser configurations or
cached data.
- **Browser troubleshooting:** for general browser troubleshooting steps such as using Incognito mode, clearing cache and cookies, or turning off browser extensions, see [Troubleshoot Google Cloud console page loading issues](https://docs.cloud.google.com/support/docs/troubleshoot-console-page-loading).
- **Verify service health:** check the [Google Cloud **Service Health** dashboard](https://status.cloud.google.com/) for ongoing BigQuery service outages.
### Connectivity and firewall requirements
If you are working behind a corporate VPN or proxy, it might block API calls
required by the console. Ensure your network administrator allowlists the
following domains:
- `https://cloudconsole-pa.clients6.google.com`
- `https://clients6.google.com`
- `https://cloudusersettings-pa.clients6.google.com`
Common symptoms of blocked API endpoints include a spinning progress indicator
for autocomplete suggestions or permission errors despite having the correct
IAM roles.
### Improve performance for large projects
If your project contains a large number of datasets or tables, the
**Explorer** tab can load slowly.
- **Use the minimal loading parameter:** you can prevent the resource tree
from pre-loading by appending `?minimal=true` to your URL. For example:
`https://console.cloud.google.com/bigquery?minimal=true&project=PROJECT_ID`
- **Unpin unused projects:** remove pinned projects that you are not actively
using to reduce the load on the **Explorer** tab.
- **Force metadata refresh:** if you modified tables through the API/CLI and
changes don't show in the console, perform a hard refresh (Ctrl+Shift+R or
Cmd+Shift+R). Use `INFORMATION_SCHEMA` queries as a definitive source of
truth.
### Troubleshoot maps and charts
- **Geospatial (TinyMap):** the geospatial visualization tab is available only if your query results contain a `GEOGRAPHY` column. Map visualization is limited to a maximum of 20,000 features or 1 million vertices.
- **Charts:** results must contain at least one numeric column to generate charts. If a "No data to display" warning appears, check for null values in the selected X-axis dimension.
### Troubleshoot data lineage and profiling
- **Lineage delay:** data lineage information isn't real-time. Allow up to 30 minutes for lineage data to synchronize.
- **Required APIs:** ensure the Dataplex API (`dataplex.googleapis.com`) and Data Catalog API (`datacatalog.googleapis.com`) are enabled in your project.
### Troubleshoot BigQuery Studio and saved queries
- **Saved query migration limit:** if you have more than 2,500 saved queries, the automated migration tool cannot process them. Contact [Cloud Customer Care](https://cloud.google.com/support) to request a manual migration.
- **Required APIs:** saving scripts and notebooks in BigQuery Studio requires the Dataform API (`dataform.googleapis.com`) to be enabled in your project.
### Contact support
If the issue persists, collect the following information before creating a
support ticket:
1. **A HAR file:** record the network request log while reproducing the issue (using the browser's developer tools Network tab with **Preserve Log** checked).
2. **Console logs:** capture JavaScript errors shown in the browser's developer console (Ctrl+Shift+I \> Console).
3. **Project ID:** the ID of the project you are working in.
4. **Tracking number:** the tracking ID shown in any error banners.
## What's next
- To learn about querying a public dataset and using the BigQuery sandbox, see [Try BigQuery using the
sandbox](https://docs.cloud.google.com/bigquery/docs/sandbox).
- To learn how to load and query data in the Google Cloud console, see [Load and query data](https://docs.cloud.google.com/bigquery/docs/quickstarts/load-data-console).