Looker vs Power BI: AI features compared

We can start by asking Copilot to review our data and suggest some visuals.

Create, update, and manage tables, columns, measures, and relationships by using natural language

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.

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.

Copilot can't currently answer questions that require generating new insights, such as anomaly detection, forecasting, or finding key influencers.

In contrast to standard SQL-powered BI experiences, Advanced Analytics supports a wide variety of data analytics—from basic computations and charting to more advanced tasks like time series forecasting.

This question asks for forecasting, which isn't currently supported.

Collaboration and reuse: Explore conversations are typically limited to one user, while data agents can be shared with other members of your organization.

App-scoped Copilot supports verified answers prepared by app authors.

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

It can query any report, semantic model, or data agent in your organization without needing to open a specific item first.

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.

Content creators who make Power BI semantic models can use OneLake integration to write model tables to Delta tables in OneLake.

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

You can add DAX query scoped measures to the model using CodeLens update model actions or the Update model with changes button.

Get information about the dashboard element with a specific id.

Visual bindings that map fields to visual roles such as Category, Values, Legend, and Tooltips

Update the dashboard element with a specific id.

PBIR file authoring and validation - creates and edits pages, visuals, filters, themes, and formatting in PBIR/PBIP format.

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

Copilot generates a report page based on your request.

The guide covers the current supported creation experience, including using the data app template, connecting to your semantic model, and telling Copilot what you want it to create.

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

Returns the contents of a LookML file.

Whether to return lineage info (upstream dataflows, tiles, data source IDs)

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.

The easiest way to start is with the Power BI Desktop Bridge CLI.

Excluded fields: Fields that the data agent should avoid

Ask a data question by using a field that isn't in the AI data schema. Copilot shouldn't return an answer.

Looker integrates with in-database analytic models, such as BigQuery Graph and Snowflake semantic views, so that you can keep your semantic definitions consistent across Looker and other BI tools, applications, or workloads that interface with your data warehouse.

Power BI supports only the following third-party semantic models:

For each question, define the expected measure, grouping, date basis, filters, and validated result.

These question-and-query pairs act as verified "gold standards" of truth that teach your agent how to handle complex business requests without guessing.

Now, when the user asks for sales by area, Copilot returns the verified answer that the model author approves.

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 time in seconds it took to load or query the visual

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

By filtering on the Copilot in Fabric operation, you can isolate all compute usage generated by Copilot requests and break it down by Power BI experience (report, semantic model, or mixed) to understand where compute demand originates.

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.

From here, you can select a semantic model and see what questions users have been asking.

Looker then uses the LookML definitions of these fields and other logic that's defined in the Explore — including field definitions, access grants or user attributes, user-defined dashboard filters, or complex Liquid or join logic — to compose the query that's sent to the database.

Row-level security (RLS) and object-level security (OLS) aren't fully supported as security features for verified answers.

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.

Users with write permissions on a semantic model can control whether users with read-only permissions can access the model through Copilot experiences.

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

Your service principal doesn't have access to any of your Power BI content and APIs.