Add conversions

.gitignore

@@ -3,4 +3,5 @@ target/
 dbt_modules/
 logs/
 .DS_Store
-dbt_packages/
+dbt_packages/
+integration_tests/package-lock.yml

CHANGELOG.md

@@ -1,3 +1,24 @@
+# dbt_pinterest v0.11.0
+[PR #34](https://github.com/fivetran/dbt_pinterest/pull/34) includes the following updates:
+
+## Feature Updates: Native Conversion Support
+- We have added the following conversion metrics to each `pinterest_ads` end model:
+  - `total_conversions`: The sum of all website conversion events.
+  - `total_conversions_quantity`: The total count of items or units involved in conversions.
+  - `total_conversions_value` (converted from `total_conversions_value_in_micro_dollar`) Total order value associated with conversions.
+- In the event that you were already passing the above fields in via our [passthrough columns](https://github.com/fivetran/dbt_pinterest/blob/main/README.md#passing-through-additional-metrics), the package will dynamically avoid "duplicate column" errors.
+
+> The above new field additions are **breaking changes** for users who were not already bringing in conversion fields via passthrough columns.
+
+## Documentation
+- Added more information about the difference in grains and their relationships in the [DECISIONLOG](https://github.com/fivetran/dbt_pinterest/blob/main/DECISIONLOG.md#pinterest-ads-grains).
+
+## Under the hood
+- Created `pinterest_ads_persist_pass_through_columns` macro to ensure that the new conversion fields are backwards compatible with users who have already included them via passthrough fields.
+- Added integrity and consistency validation tests within `integration_tests` folder for the transformation models (to be used by maintainers only).
+- Updated seed data to represent an e-commerce customer scenario.
+- Coalesces `spend` with 0 to ensure proper downstream aggregations.
+
 # dbt_pinterest v0.10.0
 [PR #30](https://github.com/fivetran/dbt_pinterest/pull/30) includes the following updates:
 

DECISIONLOG.md

@@ -2,4 +2,62 @@
 ## UTM Report Filtering
 This package contains a `pinterest_ads__url_report` which provides daily metrics for your utm compatible ads. It is important to note that not all Ads within Pinterest's `pin_promotion_report` source table leverage utm parameters. Therefore, this package takes an opinionated approach to filter out any records that do not contain utm parameters or leverage a url within the promoted pin.
 
-If you would like to leverage a report that contains all promoted pins and their daily metrics, we would suggest you leverage the `pinterest_ads__ad_report` which does not apply any filtering.
+If you would like to leverage a report that contains all promoted pins and their daily metrics, we would suggest you leverage the `pinterest_ads__ad_report` which does not apply any filtering.
+
+## Pinterest Ads Grains
+
+When working with Pinterest Ads data, here’s how the grains relate to each other and what you can expect in terms of aggregate matching:
+
+#### Advertiser Report:
+
+Grain: This is the highest level of aggregation, representing the overall performance metrics for the entire account under a single advertiser.
+Expected Aggregation: Aggregate values should match the sums across all campaigns, ad groups, pin promotions, keywords, and URLs under that advertiser.
+
+#### Campaign Report:
+
+Grain: This is a mid-level aggregation that focuses on individual campaigns under an advertiser. Each campaign aggregates data from multiple ad groups.
+Expected Aggregation: Aggregate values within a campaign should match the sums across all ad groups, pin promotions, keywords, and URLs that belong to that campaign. Campaigns under the same advertiser should aggregate up to match the advertiser report.
+
+#### Ad Group Report:
+
+Grain: This is a finer level of aggregation, representing specific ad groups within a campaign. Each ad group aggregates data from multiple pin promotions and keywords.
+Expected Aggregation: Aggregate values within an ad group should match the sums across all pin promotions and keywords under that ad group. Ad groups under the same campaign should aggregate up to match the campaign report.
+
+#### Pin Promotion Report:
+
+Grain: This grain is at the level of individual pin promotions within an ad group.
+Expected Aggregation: Aggregate values for a pin promotion should aggregate to match the corresponding ad group report. Multiple pin promotions under the same ad group should sum up to match the ad group totals.
+
+#### Keyword Report:
+
+Grain: This is at the level of individual keywords used within ad groups.
+Expected Aggregation: Aggregate values for keywords should generally aggregate to match the corresponding ad group report. However, due to how keywords might overlap across ad groups and pin promotions, there might be slight discrepancies.
+
+#### URL Report:
+
+Grain: This focuses on specific URLs that are promoted within ad groups.
+Expected Aggregation: Aggregate values should align with the ad group report for the URLs promoted within that ad group.
+
+#### Matching Aggregates:
+Advertiser vs. Campaign: Should match up, as campaigns are rolled up to the advertiser level.
+Campaign vs. Ad Group: Should match up, as ad groups are rolled up to the campaign level.
+Ad Group vs. Pin Promotion: Should match up, as pin promotions are rolled up to the ad group level.
+Ad Group vs. Keyword: May not match perfectly due to keyword overlap and variations in tracking methods.
+Ad Group vs. URL: Should match up, as URLs within the ad group should aggregate to the ad group total.
+
+Summary:
+Higher-level grains (Advertiser, Campaign) should aggregate data from lower levels (Ad Group, Pin Promotion, Keyword, URL) and generally match in totals.
+Keyword report can introduce slight discrepancies due to its more granular tracking and the potential for overlap across multiple ads or ad groups.
+Pin Promotion and URL reports should typically align with ad group totals, given that they represent specific components within an ad group.
+This structure helps ensure that the data is consistent and accurate as it aggregates from the most granular level to the highest level in the advertising funnel.
+
+#### More information on the Keyword Grain:
+It's not expected for the `keyword` table values to match up 100% to the `ad_group` table. The `keyword` table typically provides more granular data, focusing on the performance metrics at the keyword level within an ad group. Since multiple keywords can belong to a single ad group, the metrics in the `keyword` table often aggregate differently than those in the `ad group` table.
+
+Here's why the numbers might differ:
+
+The `keyword` table breaks down the performance of individual keywords within an ad group, whereas the `ad group` table aggregates the overall performance metrics for the entire ad group. Multiple keywords can trigger the same impression or click within an ad group, leading to overlap. The `ad group` table would report the aggregate metrics, while the `keyword` table may report them for each keyword, potentially leading to different totals when not aggregated.
+
+Partial Contributions: A keyword might contribute partially to certain metrics within an ad group. For example, the spend associated with a keyword might not be the same as the total spend reported at the ad group level due to allocation or prorated costs across multiple keywords.
+
+Data Allocation: Sometimes, the way costs or conversions are allocated to keywords can differ from how they are allocated at the ad group level, especially if there are shared budgets or complex attribution models in place.

README.md

@@ -31,7 +31,7 @@ The following table provides a detailed list of all tables materialized within t
 | [pinterest_ads__ad_group_report](https://fivetran.github.io/dbt_pinterest/#!/model/model.pinterest.pinterest_ads__ad_group_report)            | Each record in this table represents the daily performance of ads at the campaign, advertiser, and ad group level.|
 | [pinterest_ads__keyword_report](https://fivetran.github.io/dbt_pinterest/#!/model/model.pinterest.pinterest_ads__keyword_report)            | Each record in this table represents the daily performance of a keyword at the advertiser, campaign, ad group, and keyword level. |
 | [pinterest_ads__pin_promotion_report](https://fivetran.github.io/dbt_pinterest/#!/model/model.pinterest.pinterest_ads__pin_promotion_report)            | Each record in this table represents the daily performance of ads at the advertiser, campaign, ad group, and pin level. |
-| [pinterest_ads__url_report](https://fivetran.github.io/dbt_pinterest/#!/model/model.pinterest.pinterest_ads__url_report)            |Each record in this table represents the daily performance of ads at the advertiser, campaign, ad group, and url level. |
+| [pinterest_ads__url_report](https://fivetran.github.io/dbt_pinterest/#!/model/model.pinterest.pinterest_ads__url_report)            | Each record in this table represents the daily performance of ads at the advertiser, campaign, ad group, and url level. |
 
 <!--section-end-->
 
@@ -45,19 +45,20 @@ To use this dbt package, you must have the following:
 
 #### 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 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
+### Step 2: Install the package (skip if also using the `ad_reporting` combo package)
+Include the following pinterest_ads package version in your `packages.yml` file _if_ you are not also using the upstream [Ad Reporting combination package](https://github.com/fivetran/dbt_ad_reporting):
+
+```yml
 packages:
   - package: fivetran/pinterest
-    version: [">=0.10.0", "<0.11.0"] # we recommend using ranges to capture non-breaking changes automatically
+    version: [">=0.11.0", "<0.12.0"] # we recommend using ranges to capture non-breaking changes automatically
 ```
 
 Do NOT include the `pinterest_source` package in this file. The transformation package itself has a dependency on it and will install the source package as well.
@@ -71,15 +72,19 @@ vars:
     pinterest_schema: your_schema_name 
 ```
 
-#### Step 4: Disabling Keyword Models
+### Step 4: Disabling Keyword Models
 This package takes into consideration that not every Pinterest account tracks `keyword` performance, and allows you to disable the corresponding functionality by adding the following variable configuration:
+
 ```yml
 # dbt_project.yml
 vars:
     pinterest__using_keywords: False # Default = true
 ```
 
 ### (Optional) Step 5: Additional configurations
+
+<details open><summary>Expand/Collapse details</summary>
+
 #### Union multiple connectors
 If you have multiple pinterest connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the `source_relation` column of each model. To use this functionality, you will need to set either the `pinterest_ads_union_schemas` OR `pinterest_ads_union_databases` variables (cannot do both) in your root `dbt_project.yml` file:
 
@@ -92,10 +97,10 @@ vars:
 
 To connect your multiple schema/database sources to the package models, follow the steps outlined in the [Union Data Defined Sources Configuration](https://github.com/fivetran/dbt_fivetran_utils/tree/releases/v0.4.latest#union_data-source) section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.
 
-#### Passing Through Additional Metrics
-By default, this package will select `clicks`, `impressions`, and `cost` from the source reporting tables to store into the staging models. If you would like to pass through additional metrics to the staging models, add the below configurations to your `dbt_project.yml` file. These variables allow for the pass-through fields to be aliased (`alias`) if desired, but not required. Use the below format for declaring the respective pass-through variables:
+### Passing Through Additional Metrics
+By default, this package will select `clicks`, `impressions`, `spend` (converted from `spend_in_micro_dollar`), `total_conversions`, `total_conversions_quantity`, and `total_conversions_value` (converted from `total_conversions_value_in_micro_dollar`) from the source reporting tables to store into the staging models. If you would like to pass through additional metrics to the staging models, add the below configurations to your `dbt_project.yml` file. These variables allow for the pass-through fields to be aliased (`alias`) if desired, but not required. Use the below format for declaring the respective pass-through variables:
 
-> IMPORTANT: Make sure to exercise due diligence when adding metrics to these models. The metrics added by default (taps, impressions, and spend) have been vetted by the Fivetran team, maintaining this package for accuracy. There are metrics included within the source reports, such as metric averages, which may be inaccurately represented at the grain for reports created in this package. You must ensure that whichever metrics you pass through are appropriate to aggregate at the respective reporting levels in this package.
+> IMPORTANT: Make sure to exercise due diligence when adding metrics to these models. The metrics added by default (clicks, impressions, spend, total conversions, total conversions quantity, and total conversions value) have been vetted by the Fivetran team maintaining this package for accuracy. There are metrics included within the source reports, for example metric averages, which may be inaccurately represented at the grain for reports created in this package. You will want to ensure whichever metrics you pass through are indeed appropriate to aggregate at the respective reporting levels provided in this package.
 
 ```yml
 vars:
@@ -113,8 +118,9 @@ vars:
       - name: "other_id"
         alias: "another_id"
 ```
+
 #### Change the build schema
-By default, this package builds the Pinterest Ads staging models within a schema titled (`<target_schema>` + `_pinterest_source`) and your Pinterest Ads modeling models within a schema titled (`<target_schema>` + `_pinterest`) in your destination. If this is not where you would like your Pinterest Ads data to be written to, add the following configuration to your root `dbt_project.yml` file:
+By default, this package builds the Pinterest Ads staging models (10 views, 10 models) within a schema titled (`<target_schema>` + `_pinterest_source`) and your Pinterest Ads modeling models (6 tables) within a schema titled (`<target_schema>` + `_pinterest`) in your destination. If this is not where you would like your Pinterest Ads data to be written to, add the following configuration to your root `dbt_project.yml` file:
 
 ```yml
 models:
@@ -123,9 +129,9 @@ models:
     pinterest:
       +schema: my_new_schema_name # leave blank for just the target_schema
 ```
-    
+
 #### 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:
+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. This is not available when running the package on multiple unioned connectors.
 
 > IMPORTANT: See this project's [`dbt_project.yml`](https://github.com/fivetran/dbt_pinterest/blob/main/dbt_project.yml) variable declarations to see the expected names.
 
@@ -134,8 +140,11 @@ vars:
     pinterest_<default_source_table_name>_identifier: your_table_name 
 ```
 
-### (Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™
+</details>
+
+### (Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™   
 <details><summary>Expand for more details</summary>
+<br>
 
 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).
 
@@ -144,11 +153,11 @@ Fivetran offers the ability for you to orchestrate your dbt project through [Fiv
 ## Does this package have dependencies?
 This dbt package is dependent on the following dbt packages. 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
 packages:
     - package: fivetran/pinterest_source
-      version: [">=0.10.0", "<0.11.0"]
+      version: [">=0.11.0", "<0.12.0"]
 
     - package: fivetran/fivetran_utils
       version: [">=0.4.0", "<0.5.0"]
@@ -159,8 +168,10 @@ packages:
     - package: dbt-labs/spark_utils
       version: [">=0.3.0", "<0.4.0"]
 ```
+
 ## 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 you stay consistent with the [latest version](https://hub.getdbt.com/fivetran/pinterest/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_pinterest/blob/main/CHANGELOG.md) and release notes for more information on changes across versions.
 
 ### Opinionated Decisions
@@ -171,6 +182,11 @@ A small team of analytics engineers at Fivetran develops these dbt packages. How
 
 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) on the best workflow for contributing to a package.
 
+#### Contributors
+We thank [everyone](https://github.com/fivetran/dbt_pinterest/graphs/contributors) who has taken the time to contribute. Each PR, bug report, and feature request has made this package better and is truly appreciated.
+
+A special thank you to [Seer Interactive](https://www.seerinteractive.com/?utm_campaign=Fivetran%20%7C%20Models&utm_source=Fivetran&utm_medium=Fivetran%20Documentation), who we closely collaborated with to introduce native conversion support to our Ad packages.
+
 ## Are there any resources available?
 - If you have questions or want to reach out for help, see the [GitHub Issue](https://github.com/fivetran/dbt_pinterest/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).

dbt_project.yml

@@ -1,5 +1,5 @@
 name: 'pinterest'
-version: '0.10.0'
+version: '0.11.0'
 config-version: 2
 require-dbt-version: [">=1.3.0", "<2.0.0"]
 models: