Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://developers.google.com/bigquery/docs/bigquery-web-ui below:

Explore the Google Cloud console

Stay organized with collections Save and categorize content based on your preferences.

The Google Cloud console provides a graphical interface that you can use to create and manage BigQuery resources and run SQL queries.

To try BigQuery in the Google Cloud console, see the quickstart Query a public dataset with the Google Cloud console.

1. 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.

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

   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

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

   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

4. Enable the BigQuery API.

   Enable the API

   For new projects, the BigQuery API is automatically enabled.

5. 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.

1. Go to the Google Cloud console.

   Go to the Google Cloud console

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

3. Click Solutions > All products.

4. In the Analytics section, click BigQuery.

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

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

BigQuery opens in your most recently accessed project.

To simplify navigation, you can add (or pin) BigQuery as a top product in the navigation menu:

1. In the Google Cloud console navigation menu, hold the pointer over BigQuery.

2. Click push_pin Pin.

The BigQuery page has the following main sections:

• The BigQuery navigation menu
• The Explorer pane
• The details pane

• The Add data dialog

In the BigQuery navigation menu, you can select any option from the following categories:

• Studio: displays your datasets, tables, and other BigQuery resources. In this workspace, you can perform common BigQuery tasks such as the following:

  • Create, run, save, and share queries and Colab Enterprise notebooks.
  • Work with tables, views, routines, and other BigQuery resources.
  • See your BigQuery job history.

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

• Pipelines and integration

  • Data transfers: lets you access the BigQuery Data Transfer Service to create and configure data transfers.

  • Pipelines (Dataform): displays a list of Dataform repositories that are created for your Google Cloud project.

  • Scheduled queries: displays your scheduled queries.

  • Scheduling: provides a list of pipelines and schedules for your Google Cloud project.

• Governance

  • Sharing (Analytics Hub): displays all of the data exchanges that you can access in your Google Cloud project.

  • Policy tags: displays a list of taxonomies that you can use to create hierarchical groups of policy tags.

  • Metadata curation: lets you scan data in Cloud Storage buckets to extract and then catalog metadata.

• Administration

  • Monitoring: lets you monitor operational health and resource utilization across an organization.

  • Jobs explorer: helps you monitor jobs activity across your organization.

  • Capacity management: displays slot commitments, reservations, and reservation assignments.

  • BI Engine: lets you manage reservation capacity for query optimization using BigQuery BI Engine.

  • Disaster recovery: displays failover reservations and failover datasets.

  • Recommendations: displays a list of recommendations in your organization or project.

• Migration

  • Assessment: lets you plan and review the migration of your existing data warehouse into BigQuery.

  • SQL translation: lets you convert your Teradata SQL queries so that they work in BigQuery.

• Additional resources

  • Partner Center: provides tools and services from partners to accelerate your workflow.

  • Settings (Preview): contain the following tabs:

    • My settings: let you set default settings that are applied when you start a session in BigQuery Studio. Some settings are inherited from your project or organization, but can be overridden on the My settings page.

    • Configuration settings: let BigQuery administrators customize the BigQuery Studio experience for users within the selected project or organization. This is achieved by showing or hiding user interface elements such as Save results > CSV (Google Drive), Open in > Looker Studio, and Export > Explore with Sheets. These settings don't restrict access to the underlying data and tools, even if they are hidden in BigQuery Studio. You need the bigquery.config.get permission to make changes at the organization level.

  • Release notes: contains the latest product updates and announcements for BigQuery.

  • You can control how the BigQuery navigation menu is displayed:

    • To collapse the navigation menu so that only the icons are visible, click Toggle BigQuery navigation menu.

    • To temporarily expand the menu when it's collapsed, hold the pointer over the menu.

    • To expand the menu so that the labels stay visible, click Toggle BigQuery navigation menu.

The Explorer pane is visible when you select Studio in the navigation menu. This pane lists current Google Cloud projects and any starred projects.

To view the resources in your projects and datasets, do the following:

• To view the datasets that you have access to in a project, expand the project.

• To view tables, views, and functions in a dataset, expand the dataset.

You can also use the search box to search for resources by name (dataset, table, or view name) or by label within your current and starred projects. The search box finds the resources that directly match, or contain matches, to your search query. It might not show all the resources in the matched resource's level. To see all the resources, click Show more.

You can control how the Explorer pane is displayed:

• To collapse the Explorer pane, click the first_page icon.

• To expand the Explorer pane, click the last_page icon.

The details pane shows information about your BigQuery resources. When you select a dataset, table, view, or other resource in the Explorer pane, a new tab is displayed. On these tabs, you can view information about the resource, or you can create tables and views, modify table schemas, export data, and perform other actions.

You can drag the tab to the edge of the editor to open it in a new column so that you can compare the tabs, or drag the tab to a different position in the current or an adjacent column. This feature is in preview.

In the query editor, you can run an interactive query and explore the results in the Query results pane that opens after you run the query.

When you navigate through tabs, the resource corresponding to the focused tab is selected in the Explorer pane. If you open BigQuery using your workspace URL, then your workspace query editor tab opens, and the corresponding resource is selected in the Explorer pane.

You can use the search bar at the top of the page to search for resources (projects, datasets, or tables), documentation, and products (such as Compute Engine and Cloud Storage) across Google Cloud. You might need permissions, similar to BigQuery permissions, to access resources in different products.

In the Add data dialog, 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 service 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.

Additionally, you can do the following:

• Access and query public datasets.
• Go to the Sharing (Analytics Hub) page. For more information, see Introduction to BigQuery sharing.
• Star a project by name.

All of the work that you perform in BigQuery is done within a Google Cloud project. You can see the project name on the Google Cloud console toolbar:

Any costs that you incur for using BigQuery are charged to the billing account that's attached to the project. For more information, see BigQuery pricing.

To switch to a Google Cloud project in the Google Cloud console, follow these steps:

1. On the Google Cloud console toolbar, click the name of your project.

2. In the projects dialog, click the name of the project that you want to switch to.

   This selection becomes your active project.

You can star a Google Cloud project to the Explorer pane in the following ways:

• If you have access to a dataset or table but don't have access to the project that contains that dataset, follow these steps:

  1. On the Google Cloud console toolbar, click the name of your project.

  2. In the projects dialog, search for the project that you want to star.

  3. Hold the pointer over the name of the project, and then click star_border Add star.

• If you have the Viewer (roles/viewer) IAM role on the project, do the following:

  Method 1

  1. Switch to the project that you want to star.

  2. In the Explorer pane, hold the pointer over the name of the project that you want to star, and then click star_border Add to starred.

  Method 2

  1. In the Explorer pane, click addAdd data.

  2. In the Add data dialog, click Star a project by name.

  3. In the dialog that opens, enter the name of the project that you want to star, and click Star.

You can star a Google Cloud project only through the Google Cloud console.

To remove a project from the Explorer pane, click the name of the project, and then click star Remove from starred.

The Explorer pane lists datasets that you can browse in your current project or any projects that you starred.

• To display the datasets in a project, click arrow_right Toggle node for that project.
• To display the tables, views, and functions in a dataset, click the dataset.
• Resources are also displayed in the Explorer pane when you open them some other way. For example, when you create a table and then click Go to table, the Explorer pane highlights and shows that table under its dataset.

To display information about a resource in a project or dataset, click the name of the resource in the Explorer pane and view the details pane. The set of available actions depends on the resource. For example, the following screenshot shows the tab for a table. To refresh the contents of your resource without refreshing the entire page, click refresh Refresh:

If you have important or current projects, datasets, or tables, you can star them in the Explorer pane. To star a resource, hold the pointer over the name of the resource that you want to star, and then click star_border Star.

For more information on starring a project, see Star a project.

To view only the starred resources in the Explorer pane, click Show starred resources only in the Explorer pane.

To view all the resources in the Explorer pane, click Show all resources.

To unstar a resource, click star Unstar next to the resource.

The first time you open BigQuery, you see the welcome tab and a query editor tab, with the welcome tab in focus. The welcome tab contains the following:

• 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 Recently opened section where you can view your 10 most recently accessed resources.

• The following demo guides:

  • Run a sample query on a public dataset from Google Trends.

  • Run a Colab notebook that walks you through the basics of BigQuery DataFrames.

  • Run a Spark notebook that walks you through the basics of Spark in BigQuery.

  • Add data to BigQuery.

You may close the welcome tab, but it appears again when you refresh the page. If you want to see the welcome tab on startup, select the Show welcome page on startup checkbox. To go back to the welcome tab, click Show welcome page.

To open a query editor from the welcome tab, click add_box SQL query. To access ways to import data and search data sources that work with BigQuery, click add_box Add data.

If you open BigQuery using your workspace URL, then your workspace query editor tab opens first.

In the Google Cloud console, you can view your 10 most recently accessed resources. These resources include tables, saved queries, models, and routines.

When you create or update a resource in the console or query editor, the resource is marked as recent. For a recently opened resource to be marked as recent, you have to open it in the workspace tab. If you run a query on a resource, then the resource is not marked as recent.

To view your recently accessed resources, follow these steps:

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

2. Ensure that you are on the BigQuery welcome page. If necessary, click Show welcome page.

   In the Recently opened section, you see your 10 recently accessed resources.

You can star or open a resource in the Recently opened section. To see more options, click more_vert View actions.

When you delete a recently accessed resource through the console or query editor, it is also removed from the Recently opened section. If another user deletes your recently accessed resource or if you lose permission to that resource, you might still see the resource in the Recently opened section. When you try to open or star that resource, it disappears and you see the Resource is no longer available message.

To run the demo guide for running a sample query on a Google Trends public dataset, follow these steps. To exit the demo at any time, click close Close tour.

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

   Go to BigQuery

2. Ensure that you are on the BigQuery welcome page. If necessary, click Show welcome page.

3. Click search Open this query.

   The bigquery-public-data project is automatically added to the Explorer pane. The public project and the google_trends dataset are expanded, and the Google Trends Data dialog highlights the starred top_terms table. Additionally, the query editor is opened with a predefined query.

4. In the Google Trends Data dialog, click Next.

5. In the Google Trends Query dialog, click Next.

   To return to the previous step, click Back in the dialog.

6. In the Run this query dialog, click Try it.

   To return to the previous step, click Back in the dialog.

7. In the Query results dialog, click Done.

You can run this demo multiple times.

To view the Google Trends public dataset, in the welcome page, click View dataset.

To run the demo guide, follow these steps. To exit the demo at any time, click close Close tour. To return to the previous step, where applicable, click Back in the dialog.

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

   Go to BigQuery

2. Ensure that you are on the BigQuery welcome page. If necessary, click Show welcome page.

3. Click Open Notebook.

   The Demo notebook notebook is automatically added to Shared notebooks in the Explorer pane and opens in a tabbed editor.

4. In the Notebook dialog, click Next.

5. In the Activity dialog, review information about locating file details and version history, and then click Next.

6. In the Connect dialog, see the highlighted Connect button that you can use to connect the notebook to the runtime. Click Next.

7. In the Cell dialog, review information about cells in the notebook, and then click Next.

8. In the Code dialog, see the highlighted Code button that you can use to add a new code cell into the notebook. Click Next.

9. In the Commands dialog, see the highlighted Commands button that you can use to open a list of notebook actions. Click Next.

10. In the Terminal dialog, see the highlighted Terminal button that you can use to open a terminal to access the runtime through a command line. Click Done.

To run the demo guide, follow these steps. To exit the demo at any time, click close Close tour. To return to the previous step, where applicable, click Back in the dialog.

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

   Go to BigQuery

2. Ensure that you are on the BigQuery welcome page. If necessary, click Show welcome page.

3. Click Open notebook with Spark.

   Select a region and enable the BigQuery Unified API if you haven't already. A demo Spark notebook is automatically added to Shared notebooks in the Explorer pane and opens in a tabbed editor.

4. In the Notebook dialog, click Next.

5. In the Activity dialog, review information about locating file details and version history, and then click Next.

6. In the Connect dialog, see the highlighted Connect button that you can use to connect the notebook to the runtime. Click Next.

7. In the Cell dialog, review information about cells in the notebook, and then click Next.

8. In the Code dialog, see the highlighted Code button that you can use to add a new code cell into the notebook. Click Next.

9. In the Commands dialog, see the highlighted Commands button that you can use to open a list of notebook actions. Click Next.

10. In the Terminal dialog, see the highlighted Terminal button that you can use to open a terminal to access the runtime through a command line. Click Done.

The Add your own data section contains the demo guide for adding data to BigQuery through popular sources.

To run the demo guide, follow these steps. To exit the demo at any time, click close Close tour. To return to the previous step where applicable, click Back.

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

   Go to BigQuery

2. Ensure that you are on the BigQuery welcome page. If necessary, click Show welcome page.

3. Click Launch this guide for one of the three options: Local file, Google Drive, or Googe Cloud Storage.

4. In the Open Add Data panel dialog, click Try it.

   The source type that you selected is highlighted on the Add data pane.

5. In the Select source dialog, click Try it.

6. In the Configure source details dialog, click Next.

7. In the Configure destination details dialog, click Next.

8. In the Create table dialog, click Done.

You can run this demo guide only once through either of the three available sources in the Add your own data section. After you complete the demo, the Launch this guide buttons change to Add data and act as a shortcut to the Create table subtask.

You can control how you work with tabs in the details pane.

Whenever you select a resource or click add_box Compose new 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.

To split tabs into two panes, follow these steps:

1. Next to the tab name, click arrow_drop_down 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.

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

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.

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

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

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

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

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

2. Select cancel Close other tabs.

You can view job histories in the footer of the details pane:

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

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

To see the details of a job or to open the query in an editor, do the following:

1. In the Actions column for a job or query, click more_vert Actions.

2. Select Show job details or Open query in editor.

The job histories include all load, export, copy, and query jobs that you submitted in the past six months (up to 1,000 entries). The limit of 1,000 jobs is cumulative across all job types.

To view shortcuts in the Google Cloud console, click keyboard BigQuery Studio shortcuts. The following keyboard shortcuts are supported in the Google Cloud console:

Ctrl+Alt+t

Cmd+Option+t

Delete

or

Backspace

Delete

or

Backspace

Ctrl+Shift+f

Cmd+Shift+f

Ctrl+Shift+Space

Ctrl+Shift+Space

Ctrl+Alt+1 through 8

Cmd+Option+1 through 8

Ctrl+Alt+9

Cmd+Option+9

Ctrl+Alt+Tab

or

Ctrl+Alt+PgDn

Cmd+Shift+Option+Right arrow

Ctrl+Alt+Shift+Tab

or

Ctrl+Alt+PgUp

Cmd+Shift+Option+Left arrow

Ctrl+Alt+Shift+PgUp

Ctrl+Shift+Option+PgUp

Ctrl+Alt+Shift+PgDn

Ctrl+Shift+Option+PgDn

Shift+F10

Shift+F10

Ctrl+Enter

or

Ctrl+e

Cmd+Enter

or

Cmd+e

F1

F1

Ctrl+Alt+[

Cmd+Option+[

Ctrl+Alt+]

Cmd+Option+]

Tab

or

Ctrl+Space

Tab

or

Ctrl+Space

Ctrl+Shift+P

Cmd+Shift+P

Ctrl+/

Cmd+/

You can find Google Cloud console examples throughout the how-to guides section of the BigQuery documentation.

To see examples of loading data and querying data using the Google Cloud console, see the Load and query data with the Google Cloud console.

• To learn about querying a public dataset and using the BigQuery sandbox, see Query a public dataset with the Google Cloud console.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4