make some of those sweet sweet changes

[dave-connors-3]

well well well we meet again

[dbtnimbus]

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[dbtnimbus]

🚫 Not Reviewed

Reason: Nimbus doesn't know about this Repository.

Action: Please try uninstalling and [reinstalling the GitHub App](get a plan).

📚 Refer to the documentation for more details.
❓ If you need further assistance, please contact support.

[dbtnimbus]

👀 Taking a look at your changes! I'll provide feedback shortly.

[dbtnimbus]

The main suggestion is to remove the unnecessary empty line at the end of the file and ensure the renamed CTE includes all the necessary columns and transformations to properly stage the date dimension data. Additionally, it's worth noting that the model appears to be incomplete, so consider adding the required transformations within the CTE.

[dbtnimbus]

The added empty line at the end of the file is unnecessary and doesn't contribute to the functionality of the model. Consider removing it to maintain a clean and concise codebase. Example:

renamed as (
)

select * from renamed

Additionally, it's worth noting that this model appears to be incomplete. The renamed CTE is empty, which might indicate missing transformations. Consider adding the necessary columns and transformations within the CTE to properly stage the date dimension data. For example:

with source as (
    select * from {{ source('tpcds_core', 'date_dim') }}
),

renamed as (
    select
        d_date_sk as date_key,
        d_date as date,
        d_year as year,
        d_month as month,
        d_day as day
        -- Add more columns as needed
    from source
)

select * from renamed

More information on staging models

Staging models are crucial in dbt projects as they serve as the first layer of transformation, cleaning and preparing source data for further use. Ensure that all necessary columns are included and properly renamed to follow your project's naming conventions.

Source: dbt Labs - Staging Layer

[dbtnimbus]

👀 Taking a look at your changes! I'll provide feedback shortly.

[dbtnimbus]

My review focuses on improving readability and maintainability of the data models. I suggest explicitly defining the columns to include in each model, and adding a comment and a dbt test to explain the removal of the d_fy_year column and ensure it doesn't cause issues in downstream models.

[dbtnimbus]

Consider adding a select statement to explicitly define the columns you want to include from each referenced model. This improves readability and makes it easier to track changes in the future. For example:

select 
    stores.store_sk,
    stores.store_id,
    stores.store_name,
    -- Add other relevant columns from stores
    date_dim.date as store_closed_date

from {{ ref('stg_tpcds_core__store') }} as stores
left join {{ ref('stg_tpcds_core__date_dim') }} as date_dim
    on stores.closed_date_sk = date_dim.date_sk

This approach also allows you to rename columns if needed and ensures that only necessary columns are included in the final output.

More info

Source: dbt Labs - SQL Style Guide

[dbtnimbus]

The removal of the d_fy_year column might impact downstream models. Consider adding a comment explaining why this column was removed and how it affects the data model. Additionally, you may want to add a dbt test to ensure that this change doesn't cause unexpected issues in dependent models. For example:

-- Add a comment explaining the removal
-- d_fy_year column removed as it's no longer needed for current analysis

-- Add a dbt test in your schema.yml file
version: 2

models:
  - name: stg_tpcds_core__date_dim
    columns:
      - name: date_sk
        tests:
          - unique
          - not_null
      # Add other relevant tests

    tests:
      - dbt_utils.equal_rowcount:
          compare_model: source('tpcds_core', 'date_dim')

This test ensures that the row count in your staging model matches the source, helping to catch any potential data loss issues.

More info

Source: dbt Docs - Testing