# Quickstart for dbt and Azure Synapse Analytics [Back to guides](../guides.md) dbt platform Quickstart Beginner [Menu ]() ## Introduction In this quickstart guide, you'll learn how to use dbt with [Azure Synapse Analytics](https://azure.microsoft.com/en-us/products/synapse-analytics/). It will show you how to: * Load the Jaffle Shop sample data (provided by dbt Labs) into your Azure Synapse Analytics warehouse. * Connect dbt to Azure Synapse Analytics. * Turn a sample query into a model in your dbt project. A model in dbt is a SELECT statement. * Add tests to your models. * Document your models. * Schedule a job to run. ### Prerequisites * You have a [dbt](https://www.getdbt.com/signup/) account. * You have an Azure Synapse Analytics account. For a free trial, refer to [Synapse Analytics](https://azure.microsoft.com/en-us/free/synapse-analytics/) in the Microsoft docs. * As a Microsoft admin, you’ve enabled service principal authentication. You must add the service principal to the Synapse workspace with either a Member (recommended) or Admin permission set. For details, refer to [Create a service principal using the Azure portal](https://learn.microsoft.com/en-us/entra/identity-platform/howto-create-service-principal-portal) in the Microsoft docs. dbt needs these authentication credentials to connect to Azure Synapse Analytics. ### Related content * [dbt Learn courses](https://learn.getdbt.com) * [About continuous integration jobs](../docs/deploy/continuous-integration.md) * [Deploy jobs](../docs/deploy/deploy-jobs.md) * [Job notifications](../docs/deploy/job-notifications.md) * [Source freshness](../docs/deploy/source-freshness.md) ## Load data into your Azure Synapse Analytics 1. Log in to your [Azure portal account](https://portal.azure.com/#home). 2. On the home page, select the **SQL databases** tile. 3. From the **SQL databases** page, navigate to your organization’s workspace or create a new workspace; refer to [Create a Synapse workspace](https://learn.microsoft.com/en-us/azure/synapse-analytics/quickstart-create-workspace) in the Microsoft docs for more details. 4. From the workspace's sidebar, select **Data**. Click the three dot menu on your database and select **New SQL script** to open the SQL editor. 5. Copy these statements into the SQL editor to load the Jaffle Shop example data: ```sql CREATE TABLE dbo.customers ( [ID] [bigint], [FIRST_NAME] [varchar](8000), [LAST_NAME] [varchar](8000) ); COPY INTO [dbo].[customers] FROM 'https://dbtlabsynapsedatalake.blob.core.windows.net/dbt-quickstart-public/jaffle_shop_customers.parquet' WITH ( FILE_TYPE = 'PARQUET' ); CREATE TABLE dbo.orders ( [ID] [bigint], [USER_ID] [bigint], [ORDER_DATE] [date], [STATUS] [varchar](8000) ); COPY INTO [dbo].[orders] FROM 'https://dbtlabsynapsedatalake.blob.core.windows.net/dbt-quickstart-public/jaffle_shop_orders.parquet' WITH ( FILE_TYPE = 'PARQUET' ); CREATE TABLE dbo.payments ( [ID] [bigint], [ORDERID] [bigint], [PAYMENTMETHOD] [varchar](8000), [STATUS] [varchar](8000), [AMOUNT] [bigint], [CREATED] [date] ); COPY INTO [dbo].[payments] FROM 'https://dbtlabsynapsedatalake.blob.core.windows.net/dbt-quickstart-public/stripe_payments.parquet' WITH ( FILE_TYPE = 'PARQUET' ); ``` [![Example of loading data](/img/quickstarts/dbt-platform/example-load-data-azure-syn-analytics.png?v=2 "Example of loading data")](#)Example of loading data ## Connect dbt to Azure Synapse Analytics 1. Create a new project in dbt. Click on your account name in the left side menu, select **Account settings**, and click **+ New Project**. 2. Enter a project name and click **Continue**. 3. Choose **Synapse** as your connection and click **Next**. 4. In the **Configure your environment** section, enter the **Settings** for your new project: * **Server** — Use the service principal's **Synapse host name** value (without the trailing `, 1433` string) for the Synapse test endpoint. * **Port** — 1433 (which is the default). * **Database** — Use the service principal's **database** value for the Synapse test endpoint. 5. Enter the **User credentials** for your new project: * **Authentication** — Choose **Service Principal** from the dropdown. * **Tenant ID** — Use the service principal’s **Directory (tenant) id** as the value. * **Client ID** — Use the service principal’s **application (client) ID id** as the value. * **Client secret** — Use the service principal’s **client secret** (not the **client secret id**) as the value. 6. Click **Test connection**. This verifies that dbt can access your Azure Synapse Analytics account. 7. Click **Next** when the test succeeds. If it failed, you might need to check your Microsoft service principal. ## Set up a dbt managed repository When you develop in dbt, you can leverage [Git](../docs/platform/git/git-version-control.md) to version control your code. To connect to a repository, you can either set up a dbt-hosted [managed repository](../docs/platform/git/managed-repository.md) or directly connect to a [supported git provider](../docs/platform/git/connect-github.md). Managed repositories are a great way to trial dbt without needing to create a new repository. In the long run, it's better to connect to a supported git provider to use features like automation and [continuous integration](../docs/deploy/continuous-integration.md). To set up a managed repository: 1. Under "Setup a repository", select **Managed**. 2. Type a name for your repo such as `bbaggins-dbt-quickstart` 3. Click **Create**. It will take a few seconds for your repository to be created and imported. 4. Once you see the "Successfully imported repository," click **Continue**. ## Initialize your dbt project​ and start developing Now that you have a repository configured, you can initialize your project and start development in dbt: 1. Click **Start developing in the Studio IDE**. It might take a few minutes for your project to spin up for the first time as it establishes your git connection, clones your repo, and tests the connection to the warehouse. 2. Above the file tree to the left, click **Initialize dbt project**. This builds out your folder structure with example models. 3. Make your initial commit by clicking **Commit and sync**. Use the commit message `initial commit` and click **Commit Changes**. This creates the first commit to your managed repo and allows you to open a branch where you can add new dbt code. 4. You can now directly query data from your warehouse and execute `dbt run`. You can try this out now: * In the command line bar at the bottom, enter `dbt run` and click **Enter**. You should see a `dbt run succeeded` message. ## Build your first model 1. Under **Version Control** on the left, click **Create branch**. You can name it `add-customers-model`. You need to create a new branch since the main branch is set to read-only mode. 2. Click the three dot menu (**...**) next to the `models` directory, then select **Create file**. 3. Name the file `customers.sql`, then click **Create**. 4. Copy the following query into the file and click **Save**. customers.sql ```sql with customers as ( select ID as customer_id, FIRST_NAME as first_name, LAST_NAME as last_name from dbo.customers ), orders as ( select ID as order_id, USER_ID as customer_id, ORDER_DATE as order_date, STATUS as status from dbo.orders ), customer_orders as ( select customer_id, min(order_date) as first_order_date, max(order_date) as most_recent_order_date, count(order_id) as number_of_orders from orders group by customer_id ), final as ( select customers.customer_id, customers.first_name, customers.last_name, customer_orders.first_order_date, customer_orders.most_recent_order_date, coalesce(customer_orders.number_of_orders, 0) as number_of_orders from customers left join customer_orders on customers.customer_id = customer_orders.customer_id ) select * from final ``` 5. Enter `dbt run` in the command prompt at the bottom of the screen. You should get a successful run and see the three models. Later, you can connect your business intelligence (BI) tools to these views and tables so they only read cleaned up data rather than raw data in your BI tool. #### FAQs How can I see the SQL that dbt is running? To check out the SQL that dbt is running, you can look in: * dbt: * Within the run output, click on a model name, and then select "Details" * dbt Core: * The `target/compiled/` directory for compiled `select` statements * The `target/run/` directory for compiled `create` statements * The `logs/dbt.log` file for verbose logging. How did dbt choose which schema to build my models in? By default, dbt builds models in your target schema. To change your target schema: * If you're developing in **dbt**, these are set for each user when you first use a development environment. * If you're developing with **dbt Core**, this is the `schema:` parameter in your `profiles.yml` file. If you wish to split your models across multiple schemas, check out the docs on [using custom schemas](../docs/build/custom-schemas.md). Note: on BigQuery, `dataset` is used interchangeably with `schema`. Do I need to create my target schema before running dbt? Nope! dbt will check if the schema exists when it runs. If the schema does not exist, dbt will create it for you. If I rerun dbt, will there be any downtime as models are rebuilt? Nope! The SQL that dbt generates behind the scenes ensures that any relations are replaced atomically (i.e. your business users won't experience any downtime). The implementation of this varies on each warehouse, check out the [logs](../faqs/Runs/checking-logs.md) to see the SQL dbt is executing. What happens if the SQL in my query is bad or I get a database error? If there's a mistake in your SQL, dbt will return the error that your database returns. ```shell $ dbt run --select customers Running with dbt=1.9.0 Found 3 models, 9 tests, 0 snapshots, 0 analyses, 133 macros, 0 operations, 0 seed files, 0 sources 14:04:12 | Concurrency: 1 threads (target='dev') 14:04:12 | 14:04:12 | 1 of 1 START view model dbt_alice.customers.......................... [RUN] 14:04:13 | 1 of 1 ERROR creating view model dbt_alice.customers................. [ERROR in 0.81s] 14:04:13 | 14:04:13 | Finished running 1 view model in 1.68s. Completed with 1 error and 0 warnings: Database Error in model customers (models/customers.sql) Syntax error: Expected ")" but got identifier `your-info-12345` at [13:15] compiled SQL at target/run/jaffle_shop/customers.sql Done. PASS=0 WARN=0 ERROR=1 SKIP=0 TOTAL=1 ``` Any models downstream of this model will also be skipped. Use the error message and the [compiled SQL](../faqs/Runs/checking-logs.md) to debug any errors. ## Change the way your model is materialized One of the most powerful features of dbt is that you can change the way a model is materialized in your warehouse, simply by changing a configuration value. You can change things between tables and views by changing a keyword rather than writing the data definition language (DDL) to do this behind the scenes. By default, everything gets created as a view. You can override that at the directory level so everything in that directory will materialize to a different materialization. 1. Edit your `dbt_project.yml` file. * Update your project `name` to: dbt\_project.yml ```yaml name: 'jaffle_shop' ``` * Configure `jaffle_shop` so everything in it will be materialized as a table; and configure `example` so everything in it will be materialized as a view. Update your `models` config in the project YAML file to: dbt\_project.yml ```yaml models: jaffle_shop: +materialized: table example: +materialized: view ``` * Click **Save**. 2. Enter the `dbt run` command. Your `customers` model should now be built as a table! info To do this, dbt had to first run a `drop view` statement (or API call on BigQuery), then a `create table as` statement. 3. Edit `models/customers.sql` to override the `dbt_project.yml` for the `customers` model only by adding the following snippet to the top, and click **Save**: models/customers.sql ```sql {{ config( materialized='view' ) }} with customers as ( select id as customer_id ... ) ``` 4. Enter the `dbt run` command. Your model, `customers`, should now build as a view. * BigQuery users need to run `dbt run --full-refresh` instead of `dbt run` to full apply materialization changes. 5. Enter the `dbt run --full-refresh` command for this to take effect in your warehouse. ### FAQs What materializations are available in dbt? dbt ships with five built-in materializations: `view`, `table`, `incremental`, `ephemeral`, and `materialized_view`. Check out the documentation on [materializations](../docs/build/materializations.md) for more information on each of these options. You can also create your own [custom materializations](./create-new-materializations.md). This is an advanced feature of dbt. Which materialization should I use for my model? Start out with views, and then change models to tables when required for performance reasons (i.e. downstream queries have slowed). Check out the [docs on materializations](../docs/build/materializations.md) for advice on when to use each materialization. What model configurations exist? You can also configure: * [tags](../reference/resource-configs/tags.md) to support easy categorization and graph selection * [custom schemas](../reference/resource-properties/schema.md) to split your models across multiple schemas * [aliases](../reference/resource-configs/alias.md) if your view/table name should differ from the filename * Snippets of SQL to run at the start or end of a model, known as [hooks](../docs/build/hooks-operations.md) * Warehouse-specific configurations for performance (e.g. `sort` and `dist` keys on Redshift, `partitions` on BigQuery) Check out the docs on [model configurations](../reference/model-configs.md) to learn more. ## Delete the example models You can now delete the files that dbt created when you initialized the project: 1. Delete the `models/example/` directory. 2. Delete the `example:` key from your `dbt_project.yml` file, and any configurations that are listed under it. dbt\_project.yml ```yaml # before models: jaffle_shop: +materialized: table example: +materialized: view ``` dbt\_project.yml ```yaml # after models: jaffle_shop: +materialized: table ``` 3. Save your changes. #### FAQs How do I remove deleted models from my data warehouse? If you delete a model from your dbt project, dbt does not automatically drop the relation from your schema. This means that you can end up with extra objects in schemas that dbt creates, which can be confusing to other users. (This can also happen when you switch a model from being a view or table, to ephemeral) When you remove models from your dbt project, you should manually drop the related relations from your schema. I got an "unused model configurations" error message, what does this mean? You might have forgotten to nest your configurations under your project name, or you might be trying to apply configurations to a directory that doesn't exist. Check out this [article](https://discourse.getdbt.com/t/faq-i-got-an-unused-model-configurations-error-message-what-does-this-mean/112) to understand more. ## Build models on top of other models As a best practice in SQL, you should separate logic that cleans up your data from logic that transforms your data. You have already started doing this in the existing query by using common table expressions (CTEs). Now you can experiment by separating the logic out into separate models and using the [ref](../reference/dbt-jinja-functions/ref.md) function to build models on top of other models: [![The DAG we want for our dbt project](/img/dbt-dag.png?v=2 "The DAG we want for our dbt project")](#)The DAG we want for our dbt project 1. Create a new SQL file, `models/stg_customers.sql`, with the SQL from the `customers` CTE in our original query. 2. Create a second new SQL file, `models/stg_orders.sql`, with the SQL from the `orders` CTE in our original query. models/stg\_customers.sql ```sql select ID as customer_id, FIRST_NAME as first_name, LAST_NAME as last_name from dbo.customers ``` models/stg\_orders.sql ```sql select ID as order_id, USER_ID as customer_id, ORDER_DATE as order_date, STATUS as status from dbo.orders ``` 3. Edit the SQL in your `models/customers.sql` file as follows: models/customers.sql ```sql with customers as ( select * from {{ ref('stg_customers') }} ), orders as ( select * from {{ ref('stg_orders') }} ), customer_orders as ( select customer_id, min(order_date) as first_order_date, max(order_date) as most_recent_order_date, count(order_id) as number_of_orders from orders group by customer_id ), final as ( select customers.customer_id, customers.first_name, customers.last_name, customer_orders.first_order_date, customer_orders.most_recent_order_date, coalesce(customer_orders.number_of_orders, 0) as number_of_orders from customers left join customer_orders on customers.customer_id = customer_orders.customer_id ) select * from final ``` 4. Execute `dbt run`. This time, when you performed a `dbt run`, separate views/tables were created for `stg_customers`, `stg_orders` and `customers`. dbt inferred the order to run these models. Because `customers` depends on `stg_customers` and `stg_orders`, dbt builds `customers` last. You do not need to explicitly define these dependencies. #### FAQs How do I run one model at a time? To run one model, use the `--select` flag (or `-s` flag), followed by the name of the model: ```shell $ dbt run --select customers ``` Check out the [model selection syntax documentation](../reference/node-selection/syntax.md) for more operators and examples. Do ref-able resource names need to be unique? Within one project: yes! To build dependencies between resources (such as models, seeds, and snapshots), you need to use the `ref` function, and pass in the resource name as an argument. dbt uses that resource name to uniquely resolve the `ref` to a specific resource. As a result, these resource names need to be unique, *even if they are in distinct folders*. A resource in one project can have the same name as a resource in another project (installed as a dependency). dbt uses the project name to uniquely identify each resource. We call this "namespacing." If you `ref` a resource with a duplicated name, it will resolve to the resource within the same namespace (package or project), or raise an error because of an ambiguous reference. Use [two-argument `ref`](../reference/dbt-jinja-functions/ref.md#ref-project-specific-models) to disambiguate references by specifying the namespace. Those resource will still need to land in distinct locations in the data warehouse. Read the docs on [custom aliases](../docs/build/custom-aliases.md) and [custom schemas](../docs/build/custom-schemas.md) for details on how to achieve this. As I create more models, how should I keep my project organized? What should I name my models? There's no one best way to structure a project! Every organization is unique. If you're just getting started, check out how we (dbt Labs) [structure our dbt projects](../best-practices/how-we-structure/1-guide-overview.md). ## Add tests to your models Adding [data tests](../docs/build/data-tests.md) to a project helps validate that your models are working correctly. To add data tests to your project: 1. Create a new YAML file in the `models` directory, named `models/schema.yml` 2. Add the following contents to the file: models/schema.yml ```yaml version: 2 models: - name: customers columns: - name: customer_id data_tests: - unique - not_null - name: stg_customers columns: - name: customer_id data_tests: - unique - not_null - name: stg_orders columns: - name: order_id data_tests: - unique - not_null - name: status data_tests: - accepted_values: arguments: # available in v1.10.5 and higher. Older versions can set the as the top-level property. values: ['placed', 'shipped', 'completed', 'return_pending', 'returned'] - name: customer_id data_tests: - not_null - relationships: arguments: to: ref('stg_customers') field: customer_id ``` 3. Run `dbt test`, and confirm that all your tests passed. When you run `dbt test`, dbt iterates through your YAML files, and constructs a query for each test. Each query will return the number of records that fail the test. If this number is 0, then the test is successful. #### FAQs What tests are available for me to use in dbt? Can I add my own custom tests? Out of the box, dbt ships with the following data tests: * `unique` * `not_null` * `accepted_values` * `relationships` (for example, referential integrity) You can also write your own [custom generic tests](../docs/build/data-tests.md#generic-data-tests). Some additional generic tests have been open-sourced in the [dbt-utils package](https://github.com/dbt-labs/dbt-utils#generic-tests). Check out the docs on [packages](../docs/build/packages.md) to learn how to make these tests available in your project. How do I test one model at a time? Running tests on one model looks very similar to running a model: use the `--select` flag (or `-s` flag), followed by the name of the model: ```shell dbt test --select customers ``` Check out the [model selection syntax documentation](../reference/node-selection/syntax.md) for full syntax, and [test selection examples](../reference/node-selection/test-selection-examples.md) in particular. One of my tests failed, how can I debug it? To debug a failing test, find the SQL that dbt ran by: * dbt: * Within the test output, click on the failed test, and then select "Details". * dbt Core: * Open the file path returned as part of the error message. * Navigate to the `target/compiled/schema_tests` directory for all compiled test queries. Copy the SQL into a query editor (in dbt, you can paste it into a new `Statement`), and run the query to find the records that failed. Does my test file need to be named \`schema.yml\`? No! You can name this file whatever you want (including `whatever_you_want.yml`), so long as: * The file is in your `models/` directory¹ * The file has `.yml` extension Check out the [docs](../reference/configs-and-properties.md) for more information. ¹If you're declaring properties for seeds, snapshots, or macros, you can also place this file in the related directory — `seeds/`, `snapshots/` and `macros/` respectively. Why do model and source YAML files always start with \`version: 2\`? Once upon a time, the structure of these `.yml` files was very different (s/o to anyone who was using dbt back then!). Adding `version: 2` allowed us to make this structure more extensible. From [dbt Core v1.5](), the top-level `version:` key is optional in all resource YAML files. If present, only `version: 2` is supported. Also starting in v1.5, both the [`config-version: 2`](../reference/project-configs/config-version.md) and the top-level `version:` key in the `dbt_project.yml` are optional. Resource YAML files do not currently require this config. We only support `version: 2` if it's specified. Although we do not expect to update YAML files to `version: 3` soon, having this config will make it easier for us to introduce new structures in the future What data tests should I add to my project? We recommend that every model has a data test on a primary key, that is, a column that is `unique` and `not_null`. We also recommend that you test any assumptions on your source data. For example, if you believe that your payments can only be one of three payment methods, you should test that assumption regularly — a new payment method may introduce logic errors in your SQL. In advanced dbt projects, we recommend using [sources](../docs/build/sources.md) and running these source data-integrity tests against the sources rather than models. When should I run my data tests? You should run your data tests whenever you are writing new code (to ensure you haven't broken any existing models by changing SQL), and whenever you run your transformations in production (to ensure that your assumptions about your source data are still valid). ## Document your models Adding [documentation](../docs/build/documentation.md) to your project allows you to describe your models in rich detail, and share that information with your team. Here, we're going to add some basic documentation to our project. Update your `models/schema.yml` file to include some descriptions, such as those below. models/schema.yml ```yaml version: 2 models: - name: customers description: One record per customer columns: - name: customer_id description: Primary key data_tests: - unique - not_null - name: first_order_date description: NULL when a customer has not yet placed an order. - name: stg_customers description: This model cleans up customer data columns: - name: customer_id description: Primary key data_tests: - unique - not_null - name: stg_orders description: This model cleans up order data columns: - name: order_id description: Primary key data_tests: - unique - not_null - name: status data_tests: - accepted_values: arguments: # available in v1.10.5 and higher. Older versions can set the as the top-level property. values: ['placed', 'shipped', 'completed', 'return_pending', 'returned'] - name: customer_id data_tests: - not_null - relationships: arguments: to: ref('stg_customers') field: customer_id ``` ### View in Catalog [Catalog](../docs/explore/explore-projects.md) provides powerful tools to interact with your dbt projects, including documentation: 1. Run one of the following commands: * `dbt docs generate` if you're on dbt Core * `dbt build` if you're on the dbt Fusion engine 2. Click **Catalog** in the navigation menu to launch Catalog. 3. Catalog reflects **Production** by default. If your account has additional environments (for example, **Staging**), you can select them from the environment dropdown. [![Select an environment in Catalog.](/img/docs/collaborate/dbt-explorer/catalog-nav-dropdown.png?v=2 "Select an environment in Catalog.")](#)Select an environment in Catalog. 4. Select your project from the file tree. 5. Use the search bar or browse the resource list to find the `customers` model. 6. Click the model to view its details, including the descriptions you added. [![View your model's documentation and lineage in Catalog.](/img/docs/collaborate/dbt-explorer/example-model-details.png?v=2 "View your model's documentation and lineage in Catalog.")](#)View your model's documentation and lineage in Catalog. Catalog displays your model's description, column documentation, data tests, and lineage graph. You can also see which columns are missing documentation and track test coverage across your project. ### View in Studio IDE You can view docs directly from the IDE if you're on `Latest` or another version of dbt Core. Keep in mind that this is a legacy view and doesn't offer the same level of interactivity as Catalog. 1. In the IDE, run `dbt docs generate`. 2. From the navigation bar, click the **View docs** icon located to the right of the **branch name**. [![The View docs icon in the Studio IDE.](/img/docs/collaborate/dbt-explorer/docs-icon.png?v=2 "The View docs icon in the Studio IDE.")](#)The View docs icon in the Studio IDE. 3. From **Projects**, select your project name and expand the folders. 4. Click **models** > **marts** > **customers**. [![View your model's documentation in the legacy docs view.](/img/docs/collaborate/dbt-explorer/legacy-docs-view.png?v=2 "View your model's documentation in the legacy docs view.")](#)View your model's documentation in the legacy docs view. #### FAQs How do I write long-form explanations in my descriptions? If you need more than a sentence to explain a model, you can: 1. Split your description over multiple lines using `>`. Interior line breaks are removed and Markdown can be used. This method is recommended for simple, single-paragraph descriptions: ```yml models: - name: customers description: > Lorem ipsum **dolor** sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. ``` 2. Split your description over multiple lines using `|`. Interior line breaks are maintained and Markdown can be used. This method is recommended for more complex descriptions: ```yml models: - name: customers description: | ### Lorem ipsum * dolor sit amet, consectetur adipisicing elit, sed do eiusmod * tempor incididunt ut labore et dolore magna aliqua. ``` 3. Use a [docs block](../docs/build/documentation.md#using-docs-blocks) to write the description in a separate Markdown file. How do I access documentation in dbt Catalog? If you're using dbt to deploy your project and have a [Starter, Enterprise, or Enterprise+ plan](https://www.getdbt.com/pricing/), you can use Catalog to view your project's [resources](../docs/build/projects.md) (such as models, tests, and metrics) and their lineage to gain a better understanding of its latest production state. Access Catalog in dbt by clicking the **Catalog** link in the navigation. You can have up to 5 read-only users access the documentation for your project. dbt developer plan and dbt Core users can use [dbt Docs](../docs/explore/build-and-view-your-docs.md#dbt-docs), which generates basic documentation but it doesn't offer the same speed, metadata, or visibility as Catalog. ## Commit your changes Now that you've built your customer model, you need to commit the changes you made to the project so that the repository has your latest code. **If you edited directly in the protected primary branch:**
1. Click the **Commit and sync git** button. This action prepares your changes for commit. 2. A modal titled **Commit to a new branch** will appear. 3. In the modal window, name your new branch `add-customers-model`. This branches off from your primary branch with your new changes. 4. Add a commit message, such as "Add customers model, tests, docs" and and commit your changes. 5. Click **Merge this branch to main** to add these changes to the main branch on your repo. **If you created a new branch before editing:**
1. Since you already branched out of the primary protected branch, go to **Version Control** on the left. 2. Click **Commit and sync** to add a message. 3. Add a commit message, such as "Add customers model, tests, docs." 4. Click **Merge this branch to main** to add these changes to the main branch on your repo. ## Deploy dbt Use dbt's Scheduler to deploy your production jobs confidently and build observability into your processes. You'll learn to create a deployment environment and run a job in the following steps. ### Create a deployment environment 1. From the main menu, go to **Orchestration** > **Environments**. 2. Click **Create environment**. 3. In the **Name** field, write the name of your deployment environment. For example, "Production." 4. The **dbt version** will default to the latest available. We recommend all new projects run on the latest version of dbt. 5. Under **Deployment connection**, enter the name of the dataset you want to use as the target, such as "Analytics". This will allow dbt to build and work with that dataset. For some data warehouses, the target dataset may be referred to as a "schema". 6. Click **Save**. ### Create and run a job Jobs are a set of dbt commands that you want to run on a schedule. For example, `dbt build`. As the `jaffle_shop` business gains more customers, and those customers create more orders, you will see more records added to your source data. Because you materialized the `customers` model as a table, you'll need to periodically rebuild your table to ensure that the data stays up-to-date. This update will happen when you run a job. 1. After creating your deployment environment, you should be directed to the page for a new environment. If not, select **Orchestration** from the main menu, then click **Jobs**. 2. Click **Create job** > **Deploy job**. 3. Provide a job name (for example, "Production run") and select the environment you just created. 4. Scroll down to the **Execution settings** section. 5. Under **Commands**, add this command as part of your job if you don't see it: * `dbt build` 6. Select the **Generate docs on run** option to automatically [generate updated project docs](../docs/explore/build-and-view-your-docs.md) each time your job runs. 7. For this exercise, do *not* set a schedule for your project to run — while your organization's project should run regularly, there's no need to run this example project on a schedule. Scheduling a job is sometimes referred to as *deploying a project*. 8. Click **Save**, then click **Run now** to run your job. 9. Click the run and watch its progress under **Run summary**. 10. Once the run is complete, click **View Documentation** to see the docs for your project. Congratulations 🎉! You've just deployed your first dbt project! #### FAQs What happens if one of my runs fails? If you're using dbt, we recommend setting up email and Slack notifications (`Account Settings > Notifications`) for any failed runs. Then, debug these runs the same way you would debug any runs in development. ## Was this page helpful? YesNo [Privacy policy](https://www.getdbt.com/cloud/privacy-policy)[Create a GitHub issue](https://github.com/dbt-labs/docs.getdbt.com/issues) This site is protected by reCAPTCHA and the Google [Privacy Policy](https://policies.google.com/privacy) and [Terms of Service](https://policies.google.com/terms) apply.