In the previous section, we listed the raw tables that we have in our database. Every data modeling journey begins with similar raw tables. These tables contain the data that is needed for your application to function. Such tables often contain a lot of irregularities and inconsistencies in the way data is stored or represented. This irregularities are a result of the way data is collected and stored in the source systems. This is where the staging layer comes in. The staging layer is used to clean and rename our columns so that they are consistent and easy to work with. This layer doesn’t contain any transformations or business logic, it’s just a copy of the raw tables with some cleaning and renaming. Let us work through the raw tables in our database and create a staging layer for each of them. We prefix our table names with stg_ to indicate that they are staging tables.
You can find all the code for creating these staging tables in the models/staging/product directory of the example-dbt-project.

​
Create Staging Tables

​
stg_users

The columns of our users table need the following changes:
  • user_id
  • name - Should be renamed to user_name
  • email_address - Should be renamed to user_email
  • lat - Should be renamed to latitude
  • long - Should be renamed to longitude
  • createdAt - Should be renamed to created_at
  • updatedAt - Should be renamed to updated_at
The following dbt model creates a staging table for the users table. 
with

--
source as (
    select * from {{ source('source', 'users') }}
),

--
renamed as (
    select
        user_id,
        name as user_name,
        email_address as user_email,
        lat as latitude,
        long as longitude,
        createdAt as created_at,
        updatedAt as updated_at
    from
        source
)

--
select * from renamed

​
stg_merchants

The columns of our merchants table need the following changes:
  • merchant_id
  • merchant_name
  • geo_loc - This is a custom datatype that stores “latitude/longitude” as a single value. We should split this into two columns latitude and longitude
  • createdAt - Should be renamed to created_at
  • updatedAt - Should be renamed to updated_at
The following dbt model creates a staging table for the merchants table. 
with

--
source as (
    select * from {{ source('source', 'merchants') }}
),

--
renamed as (
    select
        merchant_id,
        name AS merchant_name,
        split(geo_loc, '/')[0] as latitude,
        split(geo_loc, '/')[1] as longitude,
        createdAt as created_at,
        updatedAt as updated_at

    from
        source
)

--
select * from renamed

​
stg_items

The columns of our items table need the following changes:
  • item_id
  • merchant_id
  • name - Should be renamed to item_name because explicit is better than implicit
  • category - Should be renamed to item_category because explicit is better than implicit
  • price - Should be renamed to base_price_usd so that it’s clear that the price is in USD
  • created_at
  • updated_at
The following dbt model creates a staging table for the items table. 
with

--
source as (
    select * from {{ source('source', 'items') }}
),

--
renamed as (
    select
        item_id,
        name as item_name,
        category as item_category,
        price as base_price_usd,
        created_at,
        updated_at

    from
        source
)

--
select * from renamed

​
stg_promotions

The columns of our promotions table need the following changes:
  • promotion_id
  • product_id - The use of the word ‘product’ is because of legacy reasons. We should rename this to item_id to match the items table
  • discount - By default we store the discount percentage as an integer e.g. If we offer 20% discount, this value is 20. We will convert this to a float by dividing by 100 to make it easier to work with. We will also rename it to discount_percentage_as_float
  • created_at
  • updated_at
The following dbt model creates a staging table for the promotions table. 
with

--
source as (
    select * from {{ source('source', 'promotions') }}
),

--
renamed as (
    select
        promotion_id,
        product_id AS item_id,
        discount / 100 AS discount_percentage_as_float,
        created_at,
        updated_at

    FROM
        source
)

--
select * from renamed

​
stg_inventory

The columns of our inventory table need the following changes:
  • inventory_id
  • merchant_id
  • item_id
  • qty - Should be renamed to quantity for clarity
  • date_acquired - Should be renamed to acquisition_date because we keep the units as suffixes
  • cost - Should be renamed to unit_cost_usd so that it’s clear that the price is in USD and we’re talking about the cost of a single unit
  • created_at
  • updated_at
We’ll also add a column total_cost_usd which is the product of quantity and unit_cost_usd since this is a common calculation that we’ll need to do. The following dbt model creates a staging table for the inventory table. 
with

--
source as (
    select * from {{ source('source', 'inventory') }}
),

--
renamed as (
    from
        inventory_id,
        merchant_id,
        item_id,
        qty as quantity,
        date_acquired as acquisition_date,
        cost as unit_cost_usd,
        qty * cost as total_cost_usd,
        created_at,
        updated_at

    from
        source
)

--
select * from renamed

​
stg_orders

The columns of our orders table need the following changes:
  • order_id
  • customer_id - Should be renamed to user_id to match the users table
  • store_id - Should be renamed to merchant_id to match the merchants table
  • date - Should be renamed to order_placed_at because it’s a timestamp and not a date, and we want to clarify that it’s the time when the order was placed
  • created_at
  • updated_at
The following dbt model creates a staging table for the orders table. 
with

--
source as (
    select * from {{ source('source', 'orders') }}
)

--
renamed as (
    select
        order_id,
        customer_id as user_id,
        store_id as merchant_id,
        date as order_placed_at,
        created_at,
        updated_at

    from
        source
)

--
select * from renamed

​
stg_order_items

The columns of our order_items table need the following changes:
  • order_item_id
  • order_id
  • product_id - Should be renamed to item_id to match the items table
  • qty - Should be renamed to quantity for clarity
  • created_at
  • updated_at
The following dbt model creates a staging table for the order_items table. 
with

--
source as (
    select * from {{ source('source', 'order_items') }}
),

--
renamed as (
    select
        order_item_id,
        order_id,
        product_id as item_id,
        qty as quantity,
        created_at,
        updated_at

    from
        source
)

--
select * from renamed

​
Next Steps

In this section, we have created staging tables for each of the raw tables in our database. These staging tables are used to clean and rename our columns so that they are consistent and easy to work with. The core of a data warehouse is made up of tables known as facts and dimensions. Facts represent actions or events, while dimensions represent the context in which these actions or events occur. In the next section, we will use the snapshot functionality in dbt to create snapshots of our staging tables. These snapshots will allow us to track changes to our data over time and create a history of our data. Such a table that contains historical records of your data is called a slowly changing dimension (SCD) table. Once we have our SCD tables, we will create our fact tables.