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feature/v2-updates #18

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1 change: 0 additions & 1 deletion .github/pull_request_template.md
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Pull Request
**Are you a current Fivetran customer?**
<!--- Please tell us your name, title and company -->

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27 changes: 27 additions & 0 deletions CHANGELOG.md
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# dbt_pinterest_source v0.6.0

## 🚨 Breaking Changes 🚨
The `pin_promotion_report_pass_through_metric` variable has been renamed to `pinterest__pin_promotion_report_passthrough_metrics`. ([#18](https://github.com/fivetran/dbt_pinterest_source/pull/18))
## 🎉 Feature Enhancements 🎉
PR [#18](https://github.com/fivetran/dbt_pinterest_source/pull/18) includes the following changes:
- Addition of the following staging models which pull from the source counterparts. The inclusion of the additional `_report` source tables is to generate a more accurate representation of the Pinterest Ads data. For example, not all Ad types are included within the `pin_promotion_report` table. Therefore, the addition of the further grain reports will allow for more flexibility and accurate Pinterest Ad reporting.
- `stg_pinterest_ads__ad_group_report`
- `stg_pinterest_ads__advertiser_report`
- `stg_pinterest_ads__campaign_report`
- `stg_pinterest_ads__keyword_report`
- `stg_pinterest_ads__advertiser_history`
- `stg_pinterest_ads__keyword_history`

- Inclusion of additional passthrough metrics:
- `pinterest__ad_group_report_passthrough_metrics`
- `pinterest__campaign_report_passthrough_metrics`
- `pinterest__advertiser_report_passthrough_metrics`
- `pinterest__keyword_report_passthrough_metrics`

- README updates for easier navigation and use of the package.
- Addition of identifier variables for each of the source tables to allow for further flexibility in source table direction within the dbt project.
- Included grain uniqueness tests for each staging table.

## Contributors
- [@bnealdefero](https://github.com/bnealdefero) ([#18](https://github.com/fivetran/dbt_pinterest_source/pull/18))

# dbt_pinterest_source v0.5.0
🎉 dbt v1.0.0 Compatibility 🎉
## 🚨 Breaking Changes 🚨
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153 changes: 80 additions & 73 deletions README.md
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[![Apache License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
# Pinterest Ads (Source)
<p align="center">
<a alt="License"
href="https://github.com/fivetran/dbt_pinterest_source/blob/main/LICENSE">
<img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" /></a>
<a alt="dbt-core">
<img src="https://img.shields.io/badge/dbt_Core™_version->=1.0.0_<2.0.0-orange.svg" /></a>
<a alt="Maintained?">
<img src="https://img.shields.io/badge/Maintained%3F-yes-green.svg" /></a>
<a alt="PRs">
<img src="https://img.shields.io/badge/Contributions-welcome-blueviolet" /></a>
</p>

# Pinterest Ads Source dbt Package ([Docs](https://fivetran.github.io/dbt_pinterest_source/))
# 📣 What does this dbt package do?
- Materializes [Pinterest Ads staging tables](https://fivetran.github.io/dbt_pinterest_source/#!/overview/pinterest_source/models/?g_v=1&g_e=seeds) which leverage data in the format described by [this ERD](https://fivetran.com/docs/applications/pinterest-ads#schemainformation). These staging tables clean, test, and prepare your Pinterest Ads data from [Fivetran's connector](https://fivetran.com/docs/applications/pinterest-ads) for analysis by doing the following:
- Name columns for consistency across all packages and for easier analysis
- Adds freshness tests to source data
- Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
- Generates a comprehensive data dictionary of your Pinterest data through the [dbt docs site](https://fivetran.github.io/dbt_pinterest_source/).
- These tables are designed to work simultaneously with our [Pinterest Ads transformation package](https://github.com/fivetran/dbt_pinterest).

# 🎯 How do I use the dbt package?
## Step 1: Prerequisites
To use this dbt package, you must have the following:
- At least one Fivetran Pinterest Ads connector syncing data into your destination.
- A **BigQuery**, **Snowflake**, **Redshift**, **PostgreSQL**, or **Databricks** destination.

This package models Pinterest Ads data from [Fivetran's connector](https://fivetran.com/docs/applications/pinterest-ads). It uses data in the format described by [this ERD](https://fivetran.com/docs/applications/pinterest-ads#schemainformation).

This package enriches your Fivetran data by doing the following:

* Adds descriptions to tables and columns that are synced using Fivetran
* Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
* Models staging tables, which will be used in our transform package

## Models

This package contains staging models, designed to work simultaneously with our [Pinterest Ads transformation package](https://github.com/fivetran/dbt_pinterest) and our [multi-platform Ad Reporting package](https://github.com/fivetran/dbt_ad_reporting). The staging models:

* Name columns consistently across all packages:
* Boolean fields are prefixed with `is_` or `has_`
* Timestamps are appended with `_at`
* ID primary keys are prefixed with the name of the table. For example, the campaign table's ID column is renamed `campaign_id`.

## Installation Instructions
Check [dbt Hub](https://hub.getdbt.com/) for the latest installation instructions, or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages.

Include in your `packages.yml`
### Databricks Dispatch Configuration
If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your `dbt_project.yml`. This is required in order for the package to accurately search for macros within the `dbt-labs/spark_utils` then the `dbt-labs/dbt_utils` packages respectively.
```yml
dispatch:
- macro_namespace: dbt_utils
search_order: ['spark_utils', 'dbt_utils']
```

## Step 2: Install the package
Include the following pinterest_source package version in your `packages.yml` file.
> TIP: Check [dbt Hub](https://hub.getdbt.com/) for the latest installation instructions or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages.
```yaml
packages:
- package: fivetran/pinterest_source
version: [">=0.5.0", "<0.6.0"]
version: [">=0.6.0", "<0.7.0"]
```

## Configuration
By default, this package will look for your Pinterest Ads data in the `pinterest_ads` schema of your [target database](https://docs.getdbt.com/docs/running-a-dbt-project/using-the-command-line-interface/configure-your-profile). If this is not where your Pinterest Ads data is, please add the following configuration to your `dbt_project.yml` file:
## Step 3: Define database and schema variables
By default, this package runs using your destination and the `pinterest` schema. If this is not where your Pinterest Ads data is (for example, if your pinterest schema is named `pinterest_fivetran`), add the following configuration to your root `dbt_project.yml` file:

```yml
# dbt_project.yml

...
config-version: 2

vars:
pinterest_database: your_database_name
pinterest_database: your_destination_name
pinterest_schema: your_schema_name
```

### Passthrough Columns
This package allows for custom columns not defined within the `stg_pinterest_ads__pin_promotion_report` model to be passed through. These custom columns may be applied using the `pin_promotion_report_pass_through_metric` variable. To apply custom passthrough columns use the below format:
## (Optional) Step 4: Additional configurations
### Passing Through Additional Metrics
By default, this package will select `clicks`, `impressions`, and `cost` from `_report` source tables used by the respective staging models. If you would like to pass through additional metrics to the staging models, add the following configuration to your `dbt_project.yml` file:

```yml
# dbt_project.yml

...
vars:
pin_promotion_report_pass_through_metric:
- 'cool_new_field'
- 'my_other_column'
- 'pass_this_through_too'

pin_promotion_report_pass_through_metric: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from pinterest.pin_promotion_report
pinterest__ad_group_report_passthrough_metrics: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from pinterest.ad_group_report
pinterest__advertiser_report_passthrough_metrics: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from pinterest.advertiser_report
pinterest__campaign_report_passthrough_metrics: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from pinterest.campaign_report
pinterest__keyword_report_passthrough_metrics: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from pinterest.keyword
```

### Changing the Build Schema
By default this package will build the Pinterest Ads staging models within a schema titled (<target_schema> + `_stg_pinterest`) in your target database. If this is not where you would like your Pinterest Ads staging data to be written to, add the following configuration to your `dbt_project.yml` file:
### Change the build schema
By default, this package builds the Pinterest Ads staging models within a schema titled (`<target_schema>` + `_pinterest_source`) in your destination. If this is not where you would like your pinterest staging data to be written to, add the following configuration to your root `dbt_project.yml` file:

```yml
# dbt_project.yml

...
models:
pinterest_source:
+schema: my_new_schema_name # leave blank for just the target_schema
```
## Database Support

### Change the source table references
If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:
> IMPORTANT: See this project's [`dbt_project.yml`](https://github.com/fivetran/dbt_pinterest_source/blob/main/dbt_project.yml) variable declarations to see the expected names.

```yml
vars:
pinterest_<default_source_table_name>_identifier: your_table_name
```

This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.
## (Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™

### Databricks Dispatch Configuration
dbt `v0.20.0` introduced a new project-level dispatch configuration that enables an "override" setting for all dispatched macros. If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your `dbt_project.yml`. This is required in order for the package to accurately search for macros within the `dbt-labs/spark_utils` then the `dbt-labs/dbt_utils` packages respectively.
Fivetran offers the ability for you to orchestrate your dbt project through [Fivetran Transformations for dbt Core™](https://fivetran.com/docs/transformations/dbt). Learn how to set up your project for orchestration through Fivetran in our [Transformations for dbt Core™ setup guides](https://fivetran.com/docs/transformations/dbt#setupguide).

# 🔍 Does this package have dependencies?
This dbt package is dependent on the following dbt packages. Please be aware that these dependencies are installed by default within this package. For more information on the following packages, refer to the [dbt hub](https://hub.getdbt.com/) site.
> IMPORTANT: If you have any of these dependent packages in your own `packages.yml` file, we highly recommend that you remove them from your root `packages.yml` to avoid package version conflicts.
```yml
# dbt_project.yml
packages:
- package: fivetran/fivetran_utils
version: [">=0.3.0", "<0.4.0"]

dispatch:
- macro_namespace: dbt_utils
search_order: ['spark_utils', 'dbt_utils']
- package: dbt-labs/dbt_utils
version: [">=0.8.0", "<0.9.0"]
```

# 🙌 How is this package maintained and can I contribute?
## Package Maintenance
The Fivetran team maintaining this package _only_ maintains the latest version of the package. We highly recommend that you stay consistent with the [latest version](https://hub.getdbt.com/fivetran/pinterest_source/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_pinterest_source/blob/main/CHANGELOG.md) and release notes for more information on changes across versions.

## Contributions
A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions!

Additional contributions to this package are very welcome! Please create issues
or open PRs against `main`. Check out
[this post](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657)
on the best workflow for contributing to a package.
We highly encourage and welcome contributions to this package. Check out [this dbt Discourse article](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) to learn how to contribute to a dbt package!

## Resources:
- Provide [feedback](https://www.surveymonkey.com/r/DQ7K7WW) on our existing dbt packages or what you'd like to see next
- Have questions or feedback, or need help? Book a time during our office hours [here](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) or email us at solutions@fivetran.com.
- Find all of Fivetran's pre-built dbt packages in our [dbt hub](https://hub.getdbt.com/fivetran/)
- Learn how to orchestrate your models with [Fivetran Transformations for dbt Core™](https://fivetran.com/docs/transformations/dbt)
- Learn more about Fivetran overall [in our docs](https://fivetran.com/docs)
- Check out [Fivetran's blog](https://fivetran.com/blog)
- Learn more about dbt [in the dbt docs](https://docs.getdbt.com/docs/introduction)
- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers
- Join the [chat](http://slack.getdbt.com/) on Slack for live discussions and support
- Find [dbt events](https://events.getdbt.com) near you
- Check out [the dbt blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices
# 🏪 Are there any resources available?
- If you have questions or want to reach out for help, please refer to the [GitHub Issue](https://github.com/fivetran/dbt_pinterest_source/issues/new/choose) section to find the right avenue of support for you.
- If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our [Feedback Form](https://www.surveymonkey.com/r/DQ7K7WW).
- Have questions or want to just say hi? Book a time during our office hours [on Calendly](https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours) or email us at solutions@fivetran.com.
18 changes: 15 additions & 3 deletions dbt_project.yml
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name: 'pinterest_source'
version: '0.5.0'
version: '0.6.0'
config-version: 2
require-dbt-version: [">=1.0.0", "<2.0.0"]
vars:
ad_group_history: "{{ source('pinterest_ads','ad_group_history') }}"
campaign_history: "{{ source('pinterest_ads','campaign_history') }}"
pin_promotion_history: "{{ source('pinterest_ads','pin_promotion_history') }}"
pin_promotion_report: "{{ source('pinterest_ads','pin_promotion_report') }}"
pin_promotion_report_pass_through_metric: []
ad_group_report: "{{ source('pinterest_ads','ad_group_report') }}"
advertiser_history: "{{ source('pinterest_ads','advertiser_history') }}"
advertiser_report: "{{ source('pinterest_ads','advertiser_report') }}"
campaign_report: "{{ source('pinterest_ads','campaign_report') }}"
keyword_history: "{{ source('pinterest_ads','keyword_history') }}"
keyword_report: "{{ source('pinterest_ads','keyword_report') }}"

pinterest__pin_promotion_report_passthrough_metrics: []
pinterest__ad_group_report_passthrough_metrics: []
pinterest__advertiser_report_passthrough_metrics: []
pinterest__campaign_report_passthrough_metrics: []
pinterest__keyword_report_passthrough_metrics: []

models:
pinterest_source:
+schema: stg_pinterest
+schema: pinterest_source
+materialized: table
tmp:
+materialized: view
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