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Write queries with exports

Exports enhance saved queries by running your saved queries and writing the output to a table or view within your data platform. Saved queries are a way to save and reuse commonly used queries in MetricFlow, exports take this functionality a step further by:

  • Enabling you to write these queries within your data platform using the dbt Cloud job scheduler.
  • Proving an integration path for tools that don't natively support the dbt Semantic Layer by exposing tables of metrics and dimensions.

Essentially, exports are like any other table in your data platform they enable you to query metric definitions through any SQL interface or connect to downstream tools without a first-class Semantic Layer integration. Running an export counts towards queried metrics usage. Querying the resulting table or view from the export does not count toward queried metric usage.


  • You have a multi-tenant dbt Cloud account on a Team or Enterprise plan. Single-tenant isn't supported at this time.
  • You use one of the following data platforms: Snowflake, BigQuery, Databricks, or Redshift.
  • You are on dbt version 1.7 or newer.
  • You have the dbt Semantic Layer configured in your dbt project.
  • You have a dbt Cloud environment with the job scheduler enabled.
  • You have a saved query and export configured in your dbt project. In your configuration, leverage caching to cache common queries, speed up performance, and reduce compute costs.
  • You have the dbt Cloud CLI installed. Note, that exports aren't supported in dbt Cloud IDE yet.

Run exports

Before you're able to run exports in development or production, you'll need to make sure you've configured saved queries and exports in your dbt project. In your saved query config, you can also leverage caching with the dbt Cloud job scheduler to cache common queries, speed up performance, and reduce compute costs.

There are two ways to run an export:

Exports in development

You can run an export in your development environment using your development credentials if you want to test the output of the export before production. You can use the following command to run exports in the dbt Cloud CLI:

dbt sl export

The following table lists the options for dbt sl export command, using the -- flag prefix to specify the parameters:

nameStringRequiredName of the export object.
saved-queryStringRequiredName of a saved query that could be used.
selectList or StringOptionalSpecify the names of exports to select from the saved query.
excludeStringOptionalSpecify the names of exports to exclude from the saved query.
export_asStringOptionalType of export to create from the export_as types available in the config. Options available are table or view.
schemaStringOptionalSchema to use for creating the table or view.
aliasStringOptionalTable alias to use to write the table or view.

You can also run any export defined for the saved query and write the table or view in your development environment. Refer to the following command example and output:


dbt sl export --saved-query sq_name


Polling for export status - query_id: 2c1W6M6qGklo1LR4QqzsH7ASGFs..
Export completed.

Use the select flag

You can have multiple exports for a saved query and by default, all exports are run for a saved query. You can use the select flag in development to select specific or multiple exports. Note, you can’t sub-select metrics or dimensions from the saved query, it’s just to change the export configuration i.e table format or schema

For example, the following command runs export_1 and export_2 and doesn't work with the --alias or --export_as flags:

dbt sl export --saved-query sq_name --select export_1,export2
Overriding export configurations

The --select flag is mainly used to include or exclude specific exports. If you need to change these settings, you can use the following flags to override export configurations:

  • --export-as Defines the materialization type (table or view) for the export. This creates a new export with its own settings and is useful for testing in development.
  • --schema Specifies the schema to use for the written table or view.
  • --alias Assigns a custom alias to the written table or view. This overrides the default export name.

Be careful. The --select flag can't be used with alias or schema.

For example, you can use the following command to create a new export named new_export as a table:

dbt sl export --saved-query sq_number1 --export-as table --alias new_export

Exports in production

Enabling and executing exports in dbt Cloud optimizes data workflows and ensures real-time data access. It enhances efficiency and governance for smarter decisions.

To enable exports in production to run saved queries and write them within your data platform, you'll need to set up dbt Cloud job scheduler and perform the following steps:

  1. Set an environment variable in dbt Cloud.
  2. Create and execute export job run.

Set environment variable

When you run a build job, any saved queries downstream of the dbt models in that job will also run. To make sure your export data is up-to-date, run the export as a downstream step (after the model).

Create and execute exports

  1. After dbt finishes building the models, the MetricFlow Server processes the exports, compiles the necessary SQL, and executes this SQL against your data platform. It directly executes a "create table" statement so the data stays within your data platform.
  2. Review the exports' execution details in the jobs logs and confirm the export was run successfully. This helps troubleshoot and to ensure accuracy. Since saved queries are integrated into the dbt DAG, all outputs related to exports are available in the job logs.
  3. Your data is now available in the data platform for querying! 🎉


 Can I have multiple exports in a single saved query?
 How do I run all exports for a saved query?
 Will I run duplicate exports if multiple models are downstream of my saved query?
 Can I reference an export as a dbt model using ref()
 How do exports help me use the dbt Semantic Layer in tools that don't support it, such as PowerBI?
 How can I select saved_queries by their resource type?