Dashboards

You can use dashboards to build data visualizations and share informative data insights with your team. AI/BI dashboards feature AI-assisted authoring, an enhanced visualization library, and a streamlined configuration experience so that you can quickly transform data into sharable insights. When published, your dashboards can be shared with anyone registered to your Databricks account, even if they don’t have access to the workspace. See Share a dashboard.

Note

AI/BI dashboards (formerly Lakeview dashboards) are now generally available.

Original Databricks SQL dashboards are now called legacy dashboards. They will continue to be supported and updated with critical bug fixes, but new functionality will be limited. You can continue to use legacy dashboards for both authoring and consumption.
Convert legacy dashboards using the migration tool or REST API. See Clone a legacy dashboard to an AI/BI dashboard for instructions on using the built-in migration tool. See Dashboard tutorials for tutorials on creating and managing dashboards using the REST API.

AI/BI dashboards have the following components:

Data: The Data tab allows users to define datasets for use in the dashboard. Datasets are bundled with dashboards when sharing, importing, or exporting them using the UI or API.
Canvas: The Canvas tab allows users to create visualizations and construct their dashboards. The canvas holds widgets that can be configured to display visualizations, filters, text, or images.

Note

You can define up to 100 datasets per dashboard. The Canvas can contain up to 100 widgets per dashboard.

View and organize dashboards

You can access dashboards from the workspace browser along with other Databricks objects.

Click Workspace in the sidebar to view dashboards from the workspace browser. Dashboards are stored in the /Workspace/Users/<username> directory by default. Users can organize dashboards into folders in the workspace browser along with other Databricks objects. See Workspace browser.
To view the dashboard listing page, click Dashboards in the sidebar.

By default, the dashboard listing page shows dashboards that you have access to sorted in reverse chronological order. You can filter the list by entering a title into the search bar, filter by last modified within a time period, or filter by owner.
Click a dashboard title to open it. If the dashboard has been published before, the published version opens. Otherwise, the draft dashboard opens.

Create a new dashboard

To create a new dashboard from the dashboard listing page, click Create near the upper-right corner of the page.

Draft and collaborate on a dashboard

New dashboards begin as a draft. You can share the draft with other users in your workspace to collaborate. You cannot share draft dashboards with users outside of the workspace. All users use their credentials to interact with the data and visualizations in dashboard drafts.

For more on permission levels, see AI/BI dashboard ACLs.

Define your datasets

Use the Data tab to define the underlying datasets for your dashboard.

You can define datasets as any of the following:

A new query against one or more tables or views.
An existing Unity Catalog table or view.

You can define datasets on any type of table or view. You can define multiple datasets by writing additional queries or selecting additional tables or views. After defining a dataset, you can use the kebab menu to the right of the dataset name to rename, clone, or delete it. You can also download the dataset as a CSV, TSV, or Excel file.

Menu shows the dataset options

Limit data access with SQL

All the data in a dashboard dataset can be accessible to dashboard viewers, even if it’s not displayed in a visualization. To prevent sensitive data from being sent to the browser, limit the columns specified in the SQL query that defines the dataset. For example, rather than selecting all columns from a table, choose only the specific columns needed for the visualizations in your SQL statement rather than table configuration.

Add or remove visualizations, text, and filter widgets on the canvas

Use the Canvas tab to construct your dashboard. Use the toolbar at the bottom of the canvas to add widgets such as visualizations, text boxes, and filters.

Visualizations

Create a visualization by adding a visualization widget to the canvas. Supported visualizations include area, bar, box, combo, counter, heatmap, histogram, line, pie, pivot, scatter, and table chart types.

Note

Queries used by visualizations do not always correspond precisely to the dataset. For example, if you apply aggregations to a visualization, the visualization shows the aggregated values.

To create a visualization, use one of the following methods:

AI-assisted visualizations: Describe the chart you want to see in natural language and let the Databricks Assistant generate a chart. After it is created, you can modify the generated chart using the configuration panel. You cannot use Assistant to create table or pivot table chart types.
Use the configuration panel: Apply additional aggregations or time bins in the visualization configuration without modifying the dataset directly. You can choose a dataset, x-axis values, y-axis values, and colors. See Dashboard visualization types for configuration details and examples of each supported visualization type. See Table options to learn how to control data presentation in table visualizations.

Note

When you apply temporal transformations in the visualization configuration, the date shown in the visualization represents the start of that period.

Static widget parameters

Parameters allow you to substitute different values into dataset queries at runtime. To learn how to set up parameters in your queries, see Work with dashboard parameters.

Static widget parameters are configured directly in a visualization widget, allowing authors to reuse datasets while presenting different views of the same result set in different visualization widgets. For example, consider a dataset showing sales trends over time across all business regions. To compare trends between Region A and Region B, you can create two visualizations with the same chart type that reference the same dataset. By adding a parameter to each visualization and selecting the specific value to display, you can show the comparison on your dashboard. Static widget parameters are non-interactive, so dashboard views cannot adjust their values.

See Static widget parameters for an example.

To learn about setting parameters in filter widgets, see Filter on parameters.

Text widgets

Markdown is a markup language for formatting text in a plain text editor. You can use markdown in text widgets to format text, insert links, and add images to your dashboard.

Add images to text widgets

Use one of the following methods to add images to your dashboard. Each method includes sample markdown syntax that edit to display your image. To resize an image, adjust the widget dimensions.

From a volume:
```
<WorkspaceURL>/ajax-api/2.0/fs/files/<Volumes/path/to/image>
```
Replace the sample text in this example with your workspace URL and the path to the image stored in a volume.

Use the following steps to get the path to your image:
1. Click Catalog in the sidebar to open the Catalog Explorer UI.
2. Click the image file in the schema browser.
3. Click the kebab menu to the right of the file name. Then, click Copy Path.
See What are Unity Catalog volumes? to learn more about storing data in volumes.

Publically hosted images:

![description](URL)` where the URL publicly hosted on the internet.

Replace the sample text with the description and URL of the image you want to show. For example, the following markdown will insert an image of the Databricks logo: ![The Databricks Logo](https://upload.wikimedia.org/wikipedia/commons/6/63/Databricks_Logo.png).

For more information on markdown syntax, see this guide.

Note

When you publish your dashboard with embedded credentials, viewers use your credentials to access the included images. If your dashboards is not published with embedded credentials, you must explicitly grant access to users for image files stored on Databricks. Use Catalog Explorer to check, grant, and revoke permissions on the image files that you want to include. Click Catalog in the sidebar to open the Catalog Explorer UI.

Filters

Filters are dashboard widgets that help viewers narrow down results and refine data in visualizations, similar to slicers in other BI tools. You can configure them to filter values based on one or more dataset columns (also referred to as fields) or parameters defined in the SQL queries that create datasets. Field filters and parameters can be combined in a single widget when using query-based parameters. See Use query-based parameters to learn how to apply a query-based parameter.

Set default values

You can set a default value in a widget for most filter types, as indicated in the following filter-type lists. To do so, select a value from the Default value drop-down in the filter widget’s configuration settings. Filters always apply to the entire dataset. If the dataset is small, the filter is applied directly in the browser to improve performance. If the dataset is larger, the filter is added to the query that is run in the SQL warehouse. See Dataset optimization and caching.

Filter on fields

Field filters refine the data presented in visualizations by filtering specific dataset fields. For example, a field filter might limit the data to a particular date range based on a date field in a dataset. Filters can be applied to the fields of one or more datasets. To connect a filter to fields from more than one dataset, add multiple Fields, up to one per dataset. The filter applies to all visualizations built on the selected datasets. When you select a value for one filter, it can dynamically change the available values shown in the drop-down menus for other filters. The following image shows a filter configuration panel set to filter on two fields.

Filter configuration shows two datasets selected. The widget drop-down shows values from both datasets

Dashboards support the following filter types for filtering on a field:

Single value
Multiple values
Date picker
Date range picker
Text entry
Range slider (does not accept default values)

Filter on parameters

Unlike field filters, which directly filter data fields, parameter filters alter the SQL query itself, allowing dynamic adjustments to the query based on user input. A filter can be applied to one or more parameters. To connect a filter to more than one parameter, add multiple Parameters in the filter configuration panel.

For filter types that accept setting a default value in the widget, the default value overrides the default value set in the query editor on the Data tab. If a filter is connected to a parameter, it runs a query against the SQL warehouse, regardless of the dataset size. See Work with dashboard parameters.

Dashboards support the following filter types for filtering on a parameter:

Single value
Date picker

You can set parameter values to accept one of the following data types:

String
Date
Date and Time
Decimal
Integer

Note

Using parameters to specify date ranges is unsupported. To specify a date range, apply filters on the fields that include the start and end dates of the desired range.

Filters and parameters in the published dashboard URLs

Filter and parameter selections are stored in the URL. Users can bookmark these URLs to save the dashboard’s state, including all filters and parameters. This way, the dashboard’s settings remain intact, allowing others to view it with the same configurations when they access the link.

Copy widgets

Use keyboard shortcuts to copy a selected widget and paste it back on the canvas. After you create a new widget, you can edit it as you would any other widget.

To clone a widget on your draft dashboard canvas, complete the following steps:

Right-click on a widget.
Click Clone.

A clone of your widget appears below the original.

Remove widgets

Delete widgets by selecting a widget and pressing the delete key on your keyboard. Or, right-click on the widget. Then, click Delete.

Cross-filtering

Cross-filtering allows users to drill down into a selected subset of data and interactively explore relationships and patterns across multiple visualizations. When a dashboard viewer clicks an element in one chart, all other charts based on the same dataset are automatically filtered on that value.

Gif shows a user clicking on a selection of a pie chart and automatically filtering values shows in a bar chart and bubble chart.

As a dashboard editor, set up cross-filtering by creating two or more charts that share the same dataset. The following chart types let viewers filter the dataset by clicking a chart element:

Bar
Heatmap
Pie
Scatter

After selection, all other visualizations based on the same dataset will be automatically updated with filtered values.

Download results

You can download datasets as CSV, TSV, or Excel files. You can download visualizations on the canvas as PNG files.

To open download options from the Canvas tab, click the kebab menu in the upper-right corner of the widget.
To open download options from a Data tab, click the kebab menu to the right of the dataset.

You can download up to approximately 1GB of results data in CSV and TSV format and up to 100,000 rows to an Excel file. The final file download size might be slightly more or less than 1GB, as the 1GB limit is applied to an earlier step than the final file download.

Publish a dashboard

Publish a dashboard to create a clean copy of the current draft. You must have at least CAN EDIT permissions to publish a dashboard.

After you publish a dashboard, the published version remains intact until you publish again, even if you make changes to the draft. You can make modifications and improvements to the draft version without affecting the published copy. Any registered user with access to the published dashboard can continue to view the published version. The published version continues to be emailed to subscribers if any exist. For more on managing dashboard access, see Share a dashboard.

When you publish a dashboard, you can choose to embed your credentials or not.

Important

Published dashboards are not versioned and should not be used for version control. You cannot revert a draft dashboard back to a previously published state.

Embed credentials: All viewers of a published dashboard can run queries using your credentials for data and compute. This allows users to see the dashboard even if they don’t have access to the originating workspace, underlying data, or SQL warehouse. This might expose data to users who have not been granted direct access to it. This is the default option.
Don’t embed credentials: All viewers of the published dashboard run queries using their own data and compute credentials. To view results in the dashboard, viewers need access to the workspace, the attached SQL warehouse, and the associated data.

See Share a published dashboard for recommendations on which setting to choose.

To publish a dashboard, complete the following steps:

Open a dashboard. If the dashboard has been published previously, the published version opens. If necessary, use the switcher at the top of the page to see the current draft version.
Click Publish. The Publish dialog appears.
Choose the credentials to use for the published dashboard. You can choose to embed your credentials or not.
Click Publish. If your dashboard is being published for the first time, a Sharing dialog opens and prompts you to share the published dashboard. See Share a dashboard for details and recommendations on sharing.

To access the published dashboard, click Published in the drop-down menu near the top of the dashboard.

Drop-down menu showing available draft and published dashboard versions.

Enable a Genie space from your dashboard

Preview

This feature is in Public Preview.

When you create a dashboard, a draft Genie space is automatically created for you. A Genie space is a no-code interface that allows business users to conduct self-serve data analytics using natural language. Publishing an associated Genie space allows viewers to explore dashboard data in a different way. Instead of gaining insights based only on what’s available on a dashboard, they engage with the data by asking their own questions for deeper analysis. See Work with an AI/BI Genie space.

The draft Genie space stays in sync with your draft dashboard as you add and remove datasets and visualizations. You can use the draft Genie space to preview and interact with your Genie space before publishing it.

To preview your draft Genie space:

Click the kebab menu in the upper-right corner of your dashboard.
Click Open the draft Genie space.

Note

You cannot edit the instructions for this Genie space from the Genie space UI. Your draft Genie space only changes when you make changes to the draft dashboard.

When you publish your dashboard, you can make this companion Genie space available for viewers. This adds an Ask Genie button that links to a published copy of the Genie space where users can ask follow-up questions.

To control whether the Genie space is available on your published dashboard:

Click Publish.
The Enable Genie toggle is on by default. Click the toggle to turn it on or off.
Click Publish.

Note

The draft and published Genie spaces are independent, much like the draft and published versions of your dashboard. Any changes made to the draft dashboard are reflected in the published Genie space only when the dashboard is republished. Draft and published Genie spaces are companions to your dashboard. They do not appear in your file browser or the Genie listing page.

Embed dashboards in websites and applications

Preview

This feature is in Public Preview.

You can embed your published dashboard into external websites and applications using an iframe. See Embed a dashboard.

Dashboard embedded in a domain external to Databricks.

If you are a workspace admin who wants to manage the external sites where dashboards can be embedded, see Manage dashboard embedding.

Schedules and subscriptions

You can set up scheduled updates to automatically refresh your dashboard’s cache and optionally send emails with a PDF of the latest dashboard to users who are subscribed to the schedule. Users with at least Can Edit permissions can create a schedule so that published dashboards with embedded credentials run periodically. Each dashboard can have up to ten schedules.

See Manage scheduled dashboard updates and subscriptions.

Export, import, or replace a dashboard

You can export and import dashboards as files to facilitate the sharing of editable dashboards across different workspaces. To transfer a dashboard to a different workspace, export it as a file and then import it into the new workspace. You can also replace dashboard files in place. That means that when you edit a dashboard file directly, you can upload that file to the original workspace and overwrite the existing file while maintaining existing sharing settings.

The following sections explain how to export and import dashboards in the UI. You can also use the Databricks API to import and export dashboards programmatically. See POST /api/2.0/workspace/import.

Export a dashboard file

From a draft dashboard, click the kebab menu at the screen’s upper-right corner, then click Export dashboard.
Confirm or cancel the action using the Export dashboard dialog. When the export succeeds, a .lvdash.json file is saved to your web browser’s default download directory.

Import a dashboard file

From the dashboards listing page, click > Import dashboard from file.
Click Choose file to open your local file dialog, then select the .lvdash.json file you want to import.
Click Import dashboard to confirm and create the dashboard.

The imported dashboard is saved to your user folder. If an imported dashboard with the same name already exists in that location, the conflict is automatically resolved by appending a number in parentheses to create a unique name.

Replace a dashboard from a file

From a draft dashboard, click the kebab menu in the screen’s upper-right corner, then click Replace dashboard.
Click Choose file to open the file dialog and select the .lvdash.json file to import.
Click Overwrite to overwrite the existing dashboard.

Managing dashboards with the REST API

See Use Databricks APIs to manage dashboards for tutorials that demonstrate how to use Databricks REST APIs to manage dashboards. The included tutorials explain how to convert legacy dashboards into Lakeview dashboards, as well as how to create, manage, and share them.

You can also manage dashboards using Terraform. For details, see the Databricks Terraform documentation.