Looker vs Tableau: AI features compared

However, you can also use Tableau Agent with live database connections, and uploaded files with the following file types:

Tableau Agent performs the steps for you, building out your flow and adding the changes to the Changes pane.

However, a data agent can connect to as many as five distinct Explores, enabling users to perform cross-domain analysis and receive more comprehensive answers.

Tableau Agent only works with the primary data source in a data blend.

You can also specify whether you want the workflow notification to include results from Gemini-powered Key driver analysis, which provides insight about why the change that triggered your workflow occurred.

Shows values for dimension members that changed the most in the same direction as the observed change in the metric.

Data freshness: Identify whether the underlying database data had changed between runs (Fresh: Yes or No), helping prevent or diagnose stale workflow alerts.

When these conditions are met, the workflow triggers a notification to the recipients that you specify using the method that you specify.

Inspector: Proactive Alerts: Includes the Data Alert Management subagent

The subagents you’ve added are assigned to your agent and visible on the Subagents panel in Agentforce Builder.

For BigQuery and Snowflake connections, the sql_analytic_model_name parameter specifies the name of an existing in-database analytic model (a BigQuery Graph or a semantic view in Snowflake) to use as the basis for a LookML view.

- SQL-based derived analytic models: You define the analytic model by using SQL Data Definition Language (DDL) statements. Looker generates the analytic model within your database by executing the SQL statements that you specify.

LookML-based derived analytic models: You define the analytic model by referencing an existing LookML Explore. Looker automatically translates your Explore topology, joins, dimensions, and measures into database-native analytic model DDL statements and creates and manages the model in your database.

This enables you to add or update data in your database with clean, prepped data from your flow each time the flow is run.

Incorporate custom logic: Convert important and commonly used custom fields or table calculations into LookML dimensions and measures.

All of these refinements become part of the metadata contained in the data source that you publish and maintain.

Update the dashboard element with a specific id.

Publishes a workbook on the specified site.

Gemini suggests LookML parameters that are based on a natural language prompt, which you can add to your project files.

After Tableau Agent creates the viz, you can interact and modify it, just like if an analyst built and shared a viz with you.

The agent runs the publishing script, which packages the component script and creates or updates a dashboard object in your configured folder.

Now available in preview, Looker Data Apps are freeform canvas dashboards that are built by local AI coding agents, such as Gemini CLI, Claude Code, or Cursor, from natural-language prompts.

Returns the contents of a LookML file.

For example, you can use upstreamTablesConnection to query the tables used by a data source, use downstreamSheetsConnection to query sheets used by a workbook, or upstreamLinkedFlows and downstreamLinkedFlows to query flows that are directly upstream or downstream of one another.

- Prebuilt skill files: Prebuilt skill files provide the AI agent with specific context, coding standards, and project-specific instructions for writing LookML. The Looker extension for VS Code automatically installs and updates skill files. You can also run the Looker: Install Skills in this Workspace or Looker: Install Skills Globally Command Palette commands in your IDE to populate or update skill files.

As Google Cloud's next-generation command-line interface, the Gemini CLI is the recommended tool for interacting with your Looker instances from the command line.

Tableau provides the tabcmd command-line utility which you can use to automate site administration tasks on your Tableau Cloud site.

Create and manage data agents for LookML dashboards: Use LookML to configure and manage version-controlled data agents for LookML dashboards.

File formats: Supported file types are text files (.txt) and Markdown files (.md).

Although Thinking mode does not produce more accurate answers than Fast mode, displaying the agent's reasoning lets you observe how Conversational Analytics interprets your query, plans its analytical steps, and references the underlying LookML models.

For BigQuery connections, if you generate a LookML project from your database, Looker will autopopulate field descriptions with the descriptions from your BigQuery column metadata, if any.

The information it indexes includes field metadata such as field captions, field descriptions from comments(first 150 characters) in Tableau Desktop or from Tableau Catalog, data roles, and data types.

Q&A Calibration (Beta): You can access this tool in the Tableau Next menu to test questions in Concierge, and then verify and calibrate results.

Turn on the following settings to enable each of their corresponding features:

Ensure the model version assigned to the deployment is supported by Tableau Server.

Latency: The end-to-end duration, in seconds, of a single conversation turn from the moment a user submits a query to when the final response is delivered.

The Conversational Analytics System Activity dashboard Token usage tab now includes observability information about top users and top conversations by token usage.

To support usage tracking and auditing, Tableau sends the requesting user's identity to your gateway as custom HTTP headers on every /chat/completions and /embeddings request so you can measure usage and active-user population directly from what the gateway receives, without relying on Tableau Server logs.

The Responses & feedback tab reports information about Conversational Analytics user behavior for users who have consented to revealing their query data to their Looker admins.

It stores the prompt and response for every LLM API call along with other audit data for a maximum of 30 days in your Salesforce Data 360 instance.

Conversational analytics uses Gemini for Google Cloud to interpret natural language questions and provide answers in Looker based on your data.

Due to our zero data retention policies in place with our third-party LLM providers, any data sent to the LLM isn’t retained and is deleted after a response is sent back.

As you interact with Tableau Agent, neither your data nor your conversations that are sent to the Large Language Model (LLM) are saved to the LLM, and no customer data is ever used to train the model.

The Gemini role still applies the gemini_in_looker permission to all models on the Looker instance; however, if needed, Looker admins can manually restrict use of Gemini in Looker to specific models by creating and assigning a Looker role with gemini_in_looker permissions on limited models.

AI Access lets a user use Tableau Agent in Dashboards, including Dashboard Overview, Dashboard Insights, and Dashboard Q&A.

Once connected, the AI agent will inherit the Looker roles and access of the user who authenticated.

To ensure minimum access to administrative tasks and content, the JWT should contain access scopes that determine which methods are accessible to the connected app or UAT.

Every action taken by an AI agent is logged in Looker System Activity and Cloud Audit Logs.

Review the Diff: Carefully review the changes proposed by the AI agent in your IDE's source control or diff viewer.

It may be possible to identify what changed to break a custom view by comparing a .twb from before and after the change.