# Quickstart for dbt and Redshift [Back to guides](../guides.md) Redshift dbt platform Quickstart Beginner [Menu ]() ## Introduction In this quickstart guide, you'll learn how to use dbt with Redshift. It will show you how to: * Set up a Redshift cluster. * Load sample data into your Redshift account. * Connect dbt to Redshift. * Take a sample query and turn it 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. Videos for you Check out [dbt Fundamentals](https://learn.getdbt.com/courses/dbt-fundamentals) for free if you're interested in course learning with videos. ### Prerequisites * You have a [dbt account](https://www.getdbt.com/signup/). * You have an AWS account with permissions to execute a CloudFormation template to create appropriate roles and a Redshift cluster. ### Related content * Learn more with [dbt Learn courses](https://learn.getdbt.com) * [CI 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) ## Create a Redshift cluster 1. Sign in to your [AWS account](https://signin.aws.amazon.com/console) as a root user or an IAM user depending on your level of access. 2. Use a CloudFormation template to quickly set up a Redshift cluster. A CloudFormation template is a configuration file that automatically spins up the necessary resources in AWS. [Start a CloudFormation stack](https://console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/new?stackName=dbt-workshop\&templateURL=https://tpch-sample-data.s3.amazonaws.com/create-dbtworkshop-infr) and you can refer to the [create-dbtworkshop-infr JSON file](https://github.com/aws-samples/aws-modernization-with-dbtlabs/blob/main/resources/cloudformation/create-dbtworkshop-infr) for more template details. tip To avoid connectivity issues with dbt, make sure to allow inbound traffic on port 5439 from [dbt's IP addresses](../docs/platform/about-platform/access-regions-ip-addresses.md) in your Redshift security groups and Network Access Control Lists (NACLs) settings. 3. Click **Next** for each page until you reach the **Select acknowledgement** checkbox. Select **I acknowledge that AWS CloudFormation might create IAM resources with custom names** and click **Create Stack**. You should land on the stack page with a CREATE\_IN\_PROGRESS status. [![Cloud Formation in Progress](/img/redshift_tutorial/images/cloud_formation_in_progress.png?v=2 "Cloud Formation in Progress")](#)Cloud Formation in Progress 4. When the stack status changes to CREATE\_COMPLETE, click the **Outputs** tab on the top to view information that you will use throughout the rest of this guide. Save those credentials for later by keeping this open in a tab. 5. Type `Redshift` in the search bar at the top and click **Amazon Redshift**. [![Click on Redshift](/img/redshift_tutorial/images/go_to_redshift.png?v=2 "Click on Redshift")](#)Click on Redshift 6. Confirm that your new Redshift cluster is listed in **Cluster overview**. Select your new cluster. The cluster name should begin with `dbtredshiftcluster-`. Then, click **Query Data**. You can choose the classic query editor or v2. We will be using the v2 version for the purpose of this guide. [![Available Redshift Cluster](/img/redshift_tutorial/images/cluster_overview.png?v=2 "Available Redshift Cluster")](#)Available Redshift Cluster 7. You might be asked to Configure account. For this sandbox environment, we recommend selecting “Configure account”. 8. Select your cluster from the list. In the **Connect to** popup, fill out the credentials from the output of the stack: * **Authentication** — Use the default which is **Database user name and password**. * **Database** — `dbtworkshop` * **User name** — `dbtadmin` * **Password** — Use the autogenerated `RSadminpassword` from the output of the stack and save it for later. [![Redshift Query Editor v2](/img/redshift_tutorial/images/redshift_query_editor.png?v=2 "Redshift Query Editor v2")](#)Redshift Query Editor v2 [![Connect to Redshift Cluster](/img/redshift_tutorial/images/connect_to_redshift_cluster.png?v=2 "Connect to Redshift Cluster")](#)Connect to Redshift Cluster 9. Click **Create connection**. ## Load data Now we are going to load our sample data into the S3 bucket that our Cloudformation template created. S3 buckets are simple and inexpensive way to store data outside of Redshift. 1. The data used in this course is stored as CSVs in a public S3 bucket. You can use the following URLs to download these files. Download these to your computer to use in the following steps. * [jaffle\_shop\_customers.csv](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/jaffle_shop_customers.csv) * [jaffle\_shop\_orders.csv](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/jaffle_shop_orders.csv) * [stripe\_payments.csv](https://dbt-tutorial-public.s3-us-west-2.amazonaws.com/stripe_payments.csv) 2. Now we are going to use the S3 bucket that you created with CloudFormation and upload the files. Go to the search bar at the top and type in `S3` and click on S3. There will be sample data in the bucket already, feel free to ignore it or use it for other modeling exploration. The bucket will be prefixed with `dbt-data-lake`. [![Go to S3](/img/redshift_tutorial/images/go_to_S3.png?v=2 "Go to S3")](#)Go to S3 3. Click on the `name of the bucket` S3 bucket. If you have multiple S3 buckets, this will be the bucket that was listed under “Workshopbucket” on the Outputs page. [![Go to your S3 Bucket](/img/redshift_tutorial/images/s3_bucket.png?v=2 "Go to your S3 Bucket")](#)Go to your S3 Bucket 4. Click **Upload**. Drag the three files into the UI and click the **Upload** button. [![Upload your CSVs](/img/redshift_tutorial/images/upload_csv.png?v=2 "Upload your CSVs")](#)Upload your CSVs 5. Remember the name of the S3 bucket for later. It should look like this: `s3://dbt-data-lake-xxxx`. You will need it for the next section. 6. Now let’s go back to the Redshift query editor. Search for Redshift in the search bar, choose your cluster, and select Query data. 7. In your query editor, execute this query below to create the schemas that we will be placing your raw data into. You can highlight the statement and then click on Run to run them individually. If you are on the Classic Query Editor, you might need to input them separately into the UI. You should see these schemas listed under `dbtworkshop`. ```sql create schema if not exists jaffle_shop; create schema if not exists stripe; ``` 8. Now create the tables in your schema with these queries using the statements below. These will be populated as tables in the respective schemas. ```sql create table jaffle_shop.customers( id integer, first_name varchar(50), last_name varchar(50) ); create table jaffle_shop.orders( id integer, user_id integer, order_date date, status varchar(50) ); create table stripe.payment( id integer, orderid integer, paymentmethod varchar(50), status varchar(50), amount integer, created date ); ``` 9. Now we need to copy the data from S3. This enables you to run queries in this guide for demonstrative purposes; it's not an example of how you would do this for a real project. Make sure to update the S3 location, iam role, and region. You can find the S3 and iam role in your outputs from the CloudFormation stack. Find the stack by searching for `CloudFormation` in the search bar, then clicking **Stacks** in the CloudFormation tile. ```sql copy jaffle_shop.customers( id, first_name, last_name) from 's3://dbt-data-lake-xxxx/jaffle_shop_customers.csv' iam_role 'arn:aws:iam::XXXXXXXXXX:role/RoleName' region 'us-east-1' delimiter ',' ignoreheader 1 acceptinvchars; copy jaffle_shop.orders(id, user_id, order_date, status) from 's3://dbt-data-lake-xxxx/jaffle_shop_orders.csv' iam_role 'arn:aws:iam::XXXXXXXXXX:role/RoleName' region 'us-east-1' delimiter ',' ignoreheader 1 acceptinvchars; copy stripe.payment(id, orderid, paymentmethod, status, amount, created) from 's3://dbt-data-lake-xxxx/stripe_payments.csv' iam_role 'arn:aws:iam::XXXXXXXXXX:role/RoleName' region 'us-east-1' delimiter ',' ignoreheader 1 Acceptinvchars; ``` Ensure that you can run a `select *` from each of the tables with the following code snippets. ```sql select * from jaffle_shop.customers; select * from jaffle_shop.orders; select * from stripe.payment; ``` ## Connect dbt to Redshift 1. Create a new project in [dbt](../docs/platform/about-platform/access-regions-ip-addresses.md). Navigate to **Account settings** (by clicking on your account name in the left side menu), and click **+ New Project**. 2. Enter a project name and click **Continue**. 3. In the **Configure your development environment** section, click the **Connection** dropdown menu and select **Add new connection**. This directs you to the connection configuration settings. 4. In the **Type** section, select **Redshift**. 5. Enter your Redshift settings. Reference your credentials you saved from the CloudFormation template. * **Hostname** — Your entire hostname. * **Port** — `5439` * **Database** (under **Optional settings**) — `dbtworkshop` [![dbt - Redshift Cluster Settings](/img/redshift_tutorial/images/dbt_cloud_redshift_account_settings.png?v=2 "dbt - Redshift Cluster Settings")](#)dbt - Redshift Cluster Settings Avoid connection issues To avoid connection issues with dbt, ensure you follow these minimal but essential AWS network setup steps because Redshift network access isn't configured automatically: * Allow inbound traffic on port `5439` from [dbt's IP addresses](../docs/platform/about-platform/access-regions-ip-addresses.md) in your Redshift security groups and Network Access Control Lists settings. * Configure your Virtual Private Cloud with the necessary route tables, IP gateways (like an internet or NAT gateway), and inbound rules. For more information, see [AWS documentation on configuring Redshift security group communication](https://docs.aws.amazon.com/redshift/latest/mgmt/rs-security-group-public-private.html). 6. Click **Save**. 7. Set up your personal user credentials by going to **Your profile** > **Credentials**. 8. Select your project that uses the Redshift connection. 9. Click the **configure your development environment and add a connection** link. This directs you to a page where you can enter your personal user credentials. 10. Set your user credentials. These credentials will be used by dbt to connect to Redshift. Those credentials (as provided in your CloudFormation output) will be: * **Username** — `dbtadmin` * **Password** — This is the autogenerated password that you used earlier in the guide * **Schema** — dbt automatically generates a schema name for you. By convention, this is `dbt_`. This is the schema connected directly to your development environment, and it's where your models will be built when running dbt within the Studio IDE. [![dbt - Redshift User credentials](/img/redshift_tutorial/images/dbt_cloud_redshift_development_credentials.png?v=2 "dbt - Redshift User credentials")](#)dbt - Redshift User credentials 11. Click **Test connection**. This verifies that dbt can access your Redshift cluster. 12. If the test succeeded, click **Save** to complete the configuration. If it failed, you might need to check your Redshift settings and credentials. ## 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**. 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: * Click **+ Create new file**, add this query to the new file, and click **Save as** to save the new file: ```sql select * from jaffle_shop.customers ``` * 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 You have two options for working with files in the Studio IDE: * Create a new branch (recommended) — Create a new branch to edit and commit your changes. Navigate to **Version Control** on the left sidebar and click **Create branch**. * Edit in the protected primary branch — If you prefer to edit, format, or lint files and execute dbt commands directly in your primary git branch. The Studio IDE prevents commits to the protected branch, so you will be prompted to commit your changes to a new branch. Name the new branch `add-customers-model`. 1. Click the **...** next to the `models` directory, then select **Create file**. 2. Name the file `customers.sql`, then click **Create**. 3. Copy the following query into the file and click **Save**. ```sql with customers as ( select id as customer_id, first_name, last_name from jaffle_shop.customers ), orders as ( select id as order_id, user_id as customer_id, order_date, status from jaffle_shop.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 1 ), 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 using (customer_id) ) select * from final ``` 4. 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, last_name from jaffle_shop.customers ``` models/stg\_orders.sql ```sql select id as order_id, user_id as customer_id, order_date, status from jaffle_shop.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 1 ), 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 using (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.