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Merge pull request #862 from dbt-labs/version-update
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‎website/docs/docs/dbt-cloud/access-control/cloud-seats-and-users.md‎

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Cloud the user is permitted to access. Users with a Developer license can be
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granted access to the Deployment and [Development](the-dbt-ide) functionality
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in dbt Cloud, whereas users with Read Only licenses are intended to view the
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[artifacts](using-dbt-cloud/artifacts.md) created in a dbt Cloud account.
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[artifacts](docs/dbt-cloud/using-dbt-cloud/artifacts) created in a dbt Cloud account.
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| Functionality | Developer User | Read Only Users |
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| ------------- | -------------- | --------------- |

‎website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-upgrading-dbt-versions.md‎

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:::info An Important Note on Packages
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If you use any packages from [dbt Hub]([https://hub.getdbt.com/](https://hub.getdbt.com/)), make sure you also upgrade to a version of the package that supports the dbt version you intend to upgrade to. You can see which dbt versions a package supports by checking on the `require-dbt-version:` in the package's dbt_project.yml file on GitHub.
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If you use any packages from [dbt Hub](https://hub.getdbt.com/), make sure you also upgrade to a version of the package that supports the dbt version you intend to upgrade to. You can see which dbt versions a package supports by checking on the `require-dbt-version:` in the package's dbt_project.yml file on GitHub.
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As an example, dbt-utils version 0.7.1 supports dbt v0.20 and v0.21, as described in its [dbt_project.yml](https://github.com/dbt-labs/dbt-utils/blob/0.7.1/dbt_project.yml).
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‎website/docs/docs/dbt-cloud/cloud-quickstart.md‎

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## Create a connection
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dbt Cloud uses this connection to _connect_ to your database when running jobs and transformation queries. Depending on the type of data warehouse you're using, you'll need to supply [different configuration parameters](connecting-your-database.md). dbt Cloud natively supports connections to Snowflake, BigQuery, Redshift, Apache Spark, Databricks, and Postgres.
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dbt Cloud uses this connection to _connect_ to your database when running jobs and transformation queries. Depending on the type of data warehouse you're using, you'll need to supply [different configuration parameters](docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-your-database). dbt Cloud natively supports connections to Snowflake, BigQuery, Redshift, Apache Spark, Databricks, and Postgres.
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After picking a data warehouse type, a form will be generated where you can populate your warehouse's credentials. These credentials are encrypted at rest, and dbt Cloud never stores credentials in plaintext.
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‎website/docs/docs/guides/migration-guide/upgrading-to-0-18-0.md‎

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- [list (ls)](commands/list)
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- [defer](defer)
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- [adapter.dispatch](adapter#dispatch)
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- [`asset-paths` config](asset-paths) (also updated [dbt_project.yml](dbt_project.yml.md) and [description](description))
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- [`asset-paths` config](asset-paths) (also updated [dbt_project.yml](reference/dbt_project.yml) and [description](description))
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- [flag for colorized logs](run#enable-or-disable-colorized-logs)
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- [`full_refresh` config](full_refresh)
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‎website/docs/learn/3-build-your-first-models.md‎

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description: With our starter project setup, it's time to get to the fun part — building models!
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---
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With our starter project setup, it's time to get to the fun part — building [models](/docs/building-models)! We're going to take the query from the [Setting up](/learn/setting-up) instructions, and turn it into a model in our dbt project.
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With our starter project setup, it's time to get to the fun part — building [models](/docs/building-a-dbt-project/building-models)! We're going to take the query from the [Setting up](/learn/setting-up) instructions, and turn it into a model in our dbt project.
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## Checkout a new git branch
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### dbt Cloud

‎website/docs/learn/4-test-and-document-your-project.md‎

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## Add tests to your models
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Adding [tests](/docs/testing) to a project helps validate that your models are working correctly. So let's add some tests to our project!
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Adding [tests](/docs/building-a-dbt-project/tests) to a project helps validate that your models are working correctly. So let's add some tests to our project!
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<CloudCore>
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<LoomVideo id="86a1e7ed19084810a7903bd31ebd83e0" />
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<FAQ src="when-to-test" />
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## Document your models
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Adding [documentation](/docs/documentation) 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.
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Adding [documentation](/docs/building-a-dbt-project/documentation) 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.
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<CloudCore>
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* Write a test that fails, for example, omit one of the order statuses in the `accepted_values` list. What does a failing test look like? Can you debug the failure?
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* Run the tests for one model only. If you grouped your `stg_` models into a directory, try running the tests for all the models in that directory.
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* Use a [docs block](/docs/documentation#using-docs-blocks) to add a Markdown description to a model.
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* Use a [docs block](/docs/building-a-dbt-project/documentation#using-docs-blocks) to add a Markdown description to a model.
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## Commit your changes
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First off, we need to commit the changes we made to our project so that our repository has our latest code.

‎website/docs/reference/project-configs/profile.md‎

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* If you are developing locally: This configuration is required, unless a command-line option (i.e. `--profile`) is supplied.
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## Related guides
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* [Connecting to your warehouse](docs/dbt-cli/configure-your-profile.md)
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* [Connecting to your warehouse](dbt-cli/configure-your-profile)
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## Recommendation
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Often an organization has only one data warehouse, so it is sensible to use your organization's name as a profile name, in `snake_case`. For example:

‎website/docs/reference/resource-configs/alias.md‎

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## Definition
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Optionally specify a custom alias for a [model](docs/docs/building-a-dbt-project/building-models.md) or [seed](docs/docs/building-a-dbt-project/seeds.md).
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Optionally specify a custom alias for a [model](docs/building-a-dbt-project/building-models) or [seed](docs/building-a-dbt-project/seeds).
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When dbt creates a relation (table/view) in a database, it creates it as: `{{ database }}.{{ schema }}.{{ identifier }}`, e.g. `analytics.finance.payments`
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‎website/docs/reference/resource-configs/database.md‎

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## Definition
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Optionally specify a custom database for a [model](docs/docs/building-a-dbt-project/building-models.md) or [seed](docs/docs/building-a-dbt-project/seeds.md). (To specify a database for a [snapshot](snapshots), use the [`target_database` config](target_database)).
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Optionally specify a custom database for a [model](docs/building-a-dbt-project/building-models) or [seed](docs/building-a-dbt-project/seeds). (To specify a database for a [snapshot](snapshots), use the [`target_database` config](target_database)).
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When dbt creates a relation (table/view) in a database, it creates it as: `{{ database }}.{{ schema }}.{{ identifier }}`, e.g. `analytics.finance.payments`
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‎website/docs/reference/resource-configs/pre-hook-post-hook.md‎

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<Changelog>
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* `v0.12.2`: The `post_hook` alias for config blocks was introduced. Prior to this, users needed to use the [alternative config syntax]() to apply pre- and post-hooks.
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* `v0.12.2`: The `post_hook` alias for config blocks was introduced. Prior to this, users needed to use the alternative config syntax to apply pre- and post-hooks.
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</Changelog>
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