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website/docs/docs/use-dbt-semantic-layer/consume-metrics.md
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| --- | ||
| title: "Consume metrics from your Semantic Layer" | ||
| description: "Learn how to query and consume metrics from your deployed dbt Semantic Layer using various tools and APIs." | ||
| sidebar_label: "Consume your metrics" | ||
| tags: [Semantic Layer] | ||
| pagination_next: "docs/use-dbt-semantic-layer/sl-faqs" | ||
| --- | ||
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| After [deploying](/docs/use-dbt-semantic-layer/deploy-sl) your dbt Semantic Layer, the next important (and fun!) step is querying and consuming the metrics you’ve defined. This page links to key resources that guide you through the process of consuming metrics across different integrations, APIs, and tools, using various different [query syntaxes](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata). | ||
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| Once your Semantic Layer is deployed, you can start querying your metrics using a variety of tools and APIs. Here are the main resources to get you started: | ||
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| ### Available integrations | ||
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| Integrate the dbt Semantic Layer with a variety of business intelligence (BI) tools and data platforms, enabling seamless metric queries within your existing workflows. Explore the following integrations: | ||
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| - [Available integrations](/docs/cloud-integrations/avail-sl-integrations) — Review a wide range of partners such as Tableau, Google Sheets, Microsoft Excel, and more, where you can query your metrics directly from the dbt Semantic Layer. | ||
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| ### Query with APIs | ||
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| To leverage the full power of the dbt Semantic Layer, you can use the dbt Semantic Layer APIs for querying metrics programmatically: | ||
| - [dbt Semantic Layer APIs](/docs/dbt-cloud-apis/sl-api-overview) — Learn how to use the dbt Semantic Layer APIs to query metrics in downstream tools, ensuring consistent and reliable data metrics. | ||
| - [JDBC API query syntax](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata) — Dive into the syntax for querying metrics with the JDBC API, with examples and detailed instructions. | ||
| - [GraphQL API query syntax](/docs/dbt-cloud-apis/sl-graphql#querying) — Learn the syntax for querying metrics via the GraphQL API, including examples and detailed instructions. | ||
| - [Python SDK](/docs/dbt-cloud-apis/sl-python#usage-examples) — Use the Python SDK library to query metrics programmatically with Python. | ||
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| ### Query during development | ||
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| For developers working within the dbt ecosystem, it’s essential to understand how to query metrics during the development phase using MetricFlow commands: | ||
| - [MetricFlow commands](/docs/build/metricflow-commands) — Learn how to use MetricFlow commands to query metrics directly during the development process, ensuring your metrics are correctly defined and working as expected. | ||
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| ## Next steps | ||
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| After understanding the basics of querying metrics, consider optimizing your setup and ensuring the integrity of your metric definitions: | ||
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| - [Optimize querying performance](/docs/use-dbt-semantic-layer/sl-cache) — Improve query speed and efficiency by using declarative caching techniques. | ||
| - [Validate semantic nodes in CI](/docs/deploy/ci-jobs#semantic-validations-in-ci) — Ensure that any changes to dbt models don’t break your metrics by validating semantic nodes in Continuous Integration (CI) jobs. | ||
| - [Build your metrics and semantic models](/docs/build/build-metrics-intro) — If you haven’t already, learn how to define and build your metrics and semantic models using your preferred development tool. |
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| --- | ||
| title: "Deploy your metrics" | ||
| id: deploy-sl | ||
| description: "Deploy the dbt Semantic Layer in dbt Cloud by running a job to materialize your metrics." | ||
| sidebar_label: "Deploy your metrics" | ||
| tags: [Semantic Layer] | ||
| pagination_next: "docs/use-dbt-semantic-layer/exports" | ||
| --- | ||
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| <!-- The below snippet can be found in the following file locations in the docs code repository) | ||
| https://github.com/dbt-labs/docs.getdbt.com/blob/current/website/snippets/_sl-run-prod-job.md | ||
| --> | ||
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| import RunProdJob from '/snippets/_sl-run-prod-job.md'; | ||
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| <RunProdJob/> | ||
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| ## Next steps | ||
| After you've executed a job and deployed your Semantic Layer: | ||
| - [Set up your Semantic Layer](/docs/use-dbt-semantic-layer/setup-sl) in dbt Cloud. | ||
| - Discover the [available integrations](/docs/cloud-integrations/avail-sl-integrations), such as Tableau, Google Sheets, Microsoft Excel, and more. | ||
| - Start querying your metrics with the [API query syntax](/docs/dbt-cloud-apis/sl-jdbc#querying-the-api-for-metric-metadata). | ||
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| ## Related docs | ||
| - [Optimize querying performance](/docs/use-dbt-semantic-layer/sl-cache) using declarative caching. | ||
| - [Validate semantic nodes in CI](/docs/deploy/ci-jobs#semantic-validations-in-ci) to ensure code changes made to dbt models don't break these metrics. | ||
| - If you haven't already, learn how to [build you metrics and semantic models](/docs/build/build-metrics-intro) in your development tool of choice. |
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| Once you’ve committed and merged your metric changes in your dbt project, you can perform a job run in your deployment environment in dbt Cloud to materialize your metrics. The deployment environment is only supported for the dbt Semantic Layer currently. | ||
| This section explains how you can perform a job run in your deployment environment in dbt Cloud to materialize and deploy your metrics. Currently, the deployment environment is only supported. | ||
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| 1. In dbt Cloud, create a new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher. | ||
| 1. Once you’ve [defined your semantic models and metrics](/guides/sl-snowflake-qs?step=10), commit and merge your metric changes in your dbt project. | ||
| 2. In dbt Cloud, create a new [deployment environment](/docs/deploy/deploy-environments#create-a-deployment-environment) or use an existing environment on dbt 1.6 or higher. | ||
| * Note — Deployment environment is currently supported (_development experience coming soon_) | ||
| 2. To create a new environment, navigate to **Deploy** in the navigation menu, select **Environments**, and then select **Create new environment**. | ||
| 3. Fill in your deployment credentials with your Snowflake username and password. You can name the schema anything you want. Click **Save** to create your new production environment. | ||
| 4. [Create a new deploy job](/docs/deploy/deploy-jobs#create-and-schedule-jobs) that runs in the environment you just created. Go back to the **Deploy** menu, select **Jobs**, select **Create job**, and click **Deploy job**. | ||
| 5. Set the job to run a `dbt build` and select the **Generate docs on run** checkbox. | ||
| 6. Run the job and make sure it runs successfully. | ||
| 3. To create a new environment, navigate to **Deploy** in the navigation menu, select **Environments**, and then select **Create new environment**. | ||
| 4. Fill in your deployment credentials with your Snowflake username and password. You can name the schema anything you want. Click **Save** to create your new production environment. | ||
| 5. [Create a new deploy job](/docs/deploy/deploy-jobs#create-and-schedule-jobs) that runs in the environment you just created. Go back to the **Deploy** menu, select **Jobs**, select **Create job**, and click **Deploy job**. | ||
| 6. Set the job to run a `dbt parse` job to parse your projects and generate a [`semantic_manifest.json` artifact](/docs/dbt-cloud-apis/sl-manifest) file. Although running `dbt build` isn't required, you can choose to do so if needed. | ||
| 7. Run the job by clicking the **Run now** button. Monitor the job's progress in real-time through the **Run summary** tab. | ||
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| Once the job completes successfully, your dbt project, including the generated documentation, will be fully deployed and available for use in your production environment. If any issues arise, review the logs to diagnose and address any errors. | ||
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| <details> | ||
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| <summary>What’s happening internally?</summary> | ||
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| - Merging the code into your main branch allows dbt Cloud to pull those changes and build the definition in the manifest produced by the run. <br /> | ||
| - Re-running the job in the deployment environment helps materialize the models, which the metrics depend on, in the data platform. It also makes sure that the manifest is up to date.<br /> | ||
| - The Semantic Layer APIs pull in the most recent manifest and enables your integration to extract metadata from it. | ||
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| </details> |