the big revamp PR!

.buildkite/pipeline.yml

@@ -58,7 +58,7 @@ steps:
     commands: |
       bash .buildkite/scripts/run_models.sh redshift
 
-  - label: ":bricks: Run Tests - Databricks"
+  - label: ":databricks: Run Tests - Databricks"
     key: "run_dbt_databricks"
     plugins:
       - docker#v3.13.0:

.buildkite/scripts/run_models.sh

@@ -19,5 +19,5 @@ dbt deps
 dbt seed --target "$db" --full-refresh
 dbt run --target "$db" --full-refresh
 dbt test --target "$db"
-dbt run --vars '{shopify__using_order_adjustment: false, shopify__using_order_line_refund: false, shopify__using_refund: false}' --target "$db" --full-refresh
+dbt run --vars '{shopify_timezone: "America/New_York", shopify_using_fulfillment_event: true}' --target "$db" --full-refresh
 dbt test --target "$db"

.github/ISSUE_TEMPLATE/config.yml

@@ -1,4 +1,7 @@
 contact_links:
+  - name: Provide feedback to our dbt package team
+    url: https://www.surveymonkey.com/r/DQ7K7WW
+    about: Fill out our survey form to provide valuable feedback to the Fivetran team developing and maintaining the dbt packages.
   - name: Ask a question during our office hours
     url: https://calendly.com/fivetran-solutions-team/fivetran-solutions-team-office-hours
     about: Schedule time during the external office hours block with the Fivetran Analytics Engineering team for support

CHANGELOG.md

@@ -1,5 +1,101 @@
-# dbt_shopify_source v0.7.0
+# dbt_shopify_source v0.8.0
+
+Lots of new features ahead!! We've revamped the package to keep up-to-date with new additions to the Shopify connector and feedback from the community. 
+
+This release includes 🚨 **Breaking Changes** 🚨.
+
+## Documentation
+- Created the [DECISIONLOG](https://github.com/fivetran/dbt_shopify_source/blob/main/DECISIONLOG.md) to log discussions and opinionated stances we took in designing the package ([PR #45](https://github.com/fivetran/dbt_shopify_source/pull/45)).
+- README updated for easier package use and navigation ([PR #38](https://github.com/fivetran/dbt_shopify_source/pull/38)).
 
+## Under the Hood 
+- Ensured Postgres compatibility ([PR #38](https://github.com/fivetran/dbt_shopify_source/pull/38)).
+- Got rid of the `shopify__using_order_adjustment`, `shopify__using_order_line_refund`, and `shopify__using_refund` variables. Instead, the package will automatically create empty versions of the related models until the source `refund`, `order_line_refund`, and `order_adjustment` tables exist in your schema. See DECISIONLOG for more details ([PR #45](https://github.com/fivetran/dbt_shopify_source/pull/45)).
+- Adjusts the organization of the `get_<table>_columns()` macros ([PR #39](https://github.com/fivetran/dbt_shopify_source/pull/39), [PR #40](https://github.com/fivetran/dbt_shopify_source/pull/40)).
+
+## Feature Updates
+- Addition of the `shopify_timezone` variable, which converts ALL timestamps included in the package (including `_fivetran_synced`) to a single target timezone in IANA Database format, ie "America/Los_Angeles" ([PR #41](https://github.com/fivetran/dbt_shopify_source/pull/41)).
+- `shopify_<default_source_table_name>_identifier` variables added if an individual source table has a different name than the package expects ([PR #38](https://github.com/fivetran/dbt_shopify_source/pull/38)).
+- The declaration of passthrough variables within your root `dbt_project.yml` has changed (but is backwards compatible). To allow for more flexibility and better tracking of passthrough columns, you will now want to define passthrough columns in the following format ([PR #40](https://github.com/fivetran/dbt_shopify_source/pull/40)):
+> This applies to all passthrough columns within the `dbt_shopify_source` package and not just the `customer_pass_through_columns` example. See the README for which models have passthrough columns.
+```yml
+vars:
+  customer_pass_through_columns:
+    - name: "my_field_to_include" # Required: Name of the field within the source.
+      alias: "field_alias" # Optional: If you wish to alias the field within the staging model.
+      transform_sql: "cast(field_alias as string)" # Optional: If you wish to define the datatype or apply a light transformation.
+```
+- The following fields have been added to (➕) or removed from (➖) their respective staging models ([PR #39](https://github.com/fivetran/dbt_shopify_source/pull/39), [PR #40](https://github.com/fivetran/dbt_shopify_source/pull/40)):
+  - `stg_shopify__order`:
+    - ➕ `total_discounts_set`
+    - ➕ `total_line_items_price_set`
+    - ➕ `total_price_usd`
+    - ➕ `total_price_set`
+    - ➕ `total_tax_set`
+    - ➕ `total_tip_received`
+    - ➕ `is_deleted`
+    - ➕ `app_id`
+    - ➕ `checkout_id`
+    - ➕ `client_details_user_agent`
+    - ➕ `customer_locale`
+    - ➕ `order_status_url`
+    - ➕ `presentment_currency`
+    - ➕ `is_confirmed`
+  - `stg_shopify__customer`:
+    - ➕ `note`
+    - ➕ `lifetime_duration`
+    - ➕ `currency`
+    - ➕ `marketing_consent_state` (coalescing of `email_marketing_consent_state` and deprecated `accepts_marketing` field)
+    - ➕ `marketing_opt_in_level` (coalescing of `email_marketing_consent_opt_in_level` and deprecated `marketing_opt_in_level` field)
+    - ➕ `marketing_consent_updated_at` (coalescing of `email_marketing_consent_consent_updated_at` and deprecated `accepts_marketing_updated_at` field)
+    - ➖ `accepts_marketing`/`has_accepted_marketing`
+    - ➖ `accepts_marketing_updated_at`
+    - ➖ `marketing_opt_in_level`
+  - `stg_shopify__order_line_refund`:
+    - ➕ `subtotal_set`
+    - ➕ `total_tax_set`
+  - `stg_shopify__order_line`:
+    - ➕ `pre_tax_price_set`
+    - ➕ `price_set`
+    - ➕ `tax_code`
+    - ➕ `total_discount_set`
+    - ➕ `variant_title`
+    - ➕ `variant_inventory_management`
+    - ➕ `properties`
+    - ( ) `is_requiring_shipping` is renamed to `is_shipping_required`
+  - `stg_shopify__product`:
+    - ➕ `status`
+  - `stg_shopify__product_variant`
+    - ➖ `old_inventory_quantity` -> coalesced with `inventory_quantity`
+    - ➕ `inventory_quantity` -> coalesced with `old_inventory_quantity`
+- The following source tables have been added to the package with respective staging models ([PR #39](https://github.com/fivetran/dbt_shopify_source/pull/39)):
+  - `abandoned_checkout`
+  - `collection_product`
+  - `collection`
+  - `customer_tag`
+  - `discount_code` -> if the table does not exist in your schema, the package will create an empty staging model and reference that ([PR #47](https://github.com/fivetran/dbt_shopify_source/pull/47/files), see [DECISIONLOG](https://github.com/fivetran/dbt_shopify/blob/main/DECISIONLOG.md))
+  - `fulfillment`
+  - `inventory_item`
+  - `inventory_level`
+  - `location`
+  - `metafield` ([#PR 49](https://github.com/fivetran/dbt_shopify_source/pull/49) as well)
+  - `order_note_attribute`
+  - `order_shipping_line`
+  - `order_shipping_tax_line`
+  - `order_tag`
+  - `order_url_tag`
+  - `price_rule`
+  - `product_image`
+  - `product_tag`
+  - `shop`
+  - `tender_transaction`
+  - `abandoned_checkout_discount_code`
+  - `order_discount_code`
+  - `tax_line`
+  - `abandoned_checkout_shipping_line` ([(PR #47)](https://github.com/fivetran/dbt_shopify_source/pull/47) as well)
+  - `fulfillment_event` -> This is NOT included by default. To include fulfillment events (used in the `shopify__daily_shop` model), set the `shopify_using_fulfillment_event` variable to `true` ([PR #48](https://github.com/fivetran/dbt_shopify_source/pull/48))
+
+# dbt_shopify_source v0.7.0
 ## 🚨 Breaking Changes 🚨:
 [PR #36](https://github.com/fivetran/dbt_shopify_source/pull/36) includes the following breaking changes:
 - Dispatch update for dbt-utils to dbt-core cross-db macros migration. Specifically `{{ dbt_utils.<macro> }}` have been updated to `{{ dbt.<macro> }}` for the below macros:

DECISIONLOG.md

@@ -0,0 +1,24 @@
+# Decision Log
+
+In creating this package, which is meant for a wide range of use cases, we had to take opinionated stances on a few different questions we came across during development. We've consolidated significant choices we made here, and will continue to update as the package evolves. 
+
+## Creating Empty Tables for Refunds, Order Line Refunds, and Order Adjustments 
+
+Source tables related to `refunds`, `order_line_refunds`, and `order_adjustments` are created in the Shopify schema dyanmically. For example, if your shop has not incurred any refunds, you will not have a `refund` table yet until you do refund an order. 
+
+Thus, the source package will create empty (1 row of all `NULL` fields) staging models if these source tables do not exist in your Shopify schema yet, and the transform package will work seamlessly with these empty models. Once `refund`, `order_line_refund`, or `order_adjustment` exists in your schema, the source and transform packages will automatically reference the new populated table(s). ([example](https://github.com/fivetran/dbt_shopify_source/blob/main/models/tmp/stg_shopify__refund_tmp.sql)). 
+
+> In previous versions of the package, you had to manually enable or disable transforms of `refund`, `order_line_refund`, or `order_adjustment` through variables. Because this required you to monitor your Shopify account/schema and update the variable(s) accordingly, we decided to pursue a more automated solution.
+
+## Keeping Deleted Entities 
+
+Instead of automatically filtering out records where `_fivetran_deleted` is `true`, the package keeps these soft-deleted records, as they may persist as foreign keys in other tables. The package merely renames the deleted-flag to `is_deleted`, which you can filter out if you choose.
+
+## Accepted Value Test Severity
+
+We test the following columns for accepted values because their values are hard-coded to be pivoted out into columns and/or used as `JOIN` conditions in downstream models.
+- `stg_shopify__price_rule.target_type`: accepted values are `line_item`, `shipping_line`
+- `stg_shopify__price_rule.value_type`: accepted values are `percentage`, `fixed_amount`
+- `stg_shopify__fulfillment.status`: accepted values are `pending`, `open`, `success`, `cancelled`, `error`, `failure`
+
+We have chosen to make the severity of these tests `warn`, as non-accepted values will be filtered out in the transformation models. They will not introduce erroneous data.