Get practice building a dbt project from the ground up. Apply your skills at loading different types of data into the dbt project and setting up a variety of staging dbt models. This chapter focuses on the E and L parts of the ELT process.

Introducing the dbt case study

Check dbt installation

Initialize the dbt project

Setting up the dbt project and loading data

Load distribution center as a dbt seed file

Create distribution center staging model

Load and create user model via dbt source

More practice with dbt source and staging models

Debug common errors

Setting up dbt

Dive deep into the weeds of dbt data modeling. Build the data pipeline from preliminary staging models to the final data mart models for answering critical business needs. Along the way, get experience creating data tests to guardrail against data quality drift.

Creating dbt descriptions and tests

Add dbt column descriptions

Add dbt column tests

Building dbt data marts and snapshot models

Refresher on dbt model hierarchy

Create mart model for customers

Create mart model for products

Debugging common errors

Debugging more errors

Building dbt snapshot models

Building dbt models

Learn to enhance your dbt projects with Jinja by streamlining code, reducing redundancy, and improving maintainability. You'll practice using variables, loops, and macros to build more efficient transformation workflows.

Jinja introduceren met dbt

looker_ecommerce:
  outputs:
    dev:
      type: duckdb
      path: /home/repl/dbt.duckdb
  target: dev



# Name your project! Project names should contain only lowercase characters
# and underscores. A good package name should reflect your organization's
# name or the intended use of these models
name: 'looker_ecommerce'
version: '1.0.0'

# This setting configures which "profile" dbt uses for this project.
profile: 'looker_ecommerce'

# These configurations specify where dbt should look for different types of files.
# The `model-paths` config, for example, states that models in this project can be
# found in the "models/" directory. You probably won't need to change these!
model-paths: ["models"]
analysis-paths: ["analyses"]
test-paths: ["tests"]
seed-paths: ["seeds"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]

clean-targets:         # directories to be removed by `dbt clean`
  - "target"
  - "dbt_packages"


version: 2

seeds:
  - name: looker__distribution_centers


version: 2

sources:
  - name: looker_ecommerce
    tables:
      - name: users
      - name: events
      - name: inventory_items
      - name: order_items
      - name: orders
      - name: products

version: 2

models:
  - name: stg_looker__distribution_centers
    description: Distribution center name and location
    columns:
      - name: id 
        description: Unique ID for the distribution center
        data_tests:
          - unique
          - not_null          
      - name: name 
        description: Distribution center name
        data_tests:
          - not_null
      - name: latitude
        description: Distibution center location latitude
      - name: longitude
        description: Distribution center location longitude

  - name: stg_looker__users
    description: Users demographics and location information
    columns:
      - name: id
        description: Unique ID for the user
        data_tests:
          - unique
          - not_null
      - name: first_name
        description: User first name
        data_tests:
          - not_null
      - name: last_name
        description: User last name
        data_tests:
          - not_null
      - name: email
        description: User email
        data_tests:
          - not_null
      - name: age
        description: User age in years
        data_tests:
          - not_null
      - name: gender
        description: User gender
        data_tests:
          - accepted_values:
              values: ['F', 'M', null]
      - name: state
        description: User residing state
      - name: street_address
        description: User residing street address
      - name: postal_code
        description: User residing postal code
      - name: city
        description: User residing city 
      - name: country
        description: User residing country
      - name: latitude
        description: User residing address latitude
      - name: longitude
        description: User residing address longitude
      - name: traffic_source
        description: Traffic source for user information 
        data_tests:
          - not_null
          - accepted_values:
              values: ['Display', 'Email', 'Facebook', 'Organic', 'Search']
      - name: created_at
        description: User created at timestamp
        data_tests:
          - not_null

  - name: stg_looker__events
    description: User event data
    columns: 
      - name: id
        description: Unique ID for event entity
        data_tests:
          - unique
          - not_null
      - name: user_id
        description: User ID tied to the event 
      - name: sequence_number
        description: Sequence number for the event 
      - name: session_id
        description: Event session ID 
        data_tests:
          - not_null
      - name: created_at
        description: Event created at timestamp
        data_tests:
          - not_null
      - name: ip_address        
        description: Event IP address
        data_tests:
          - not_null
      - name: city
        description: City where event occurred 
      - name: state
        description: State where event occurred
      - name: postal_code
        description: Postal code where event occurred
      - name: browser
        description: Browser used for event 
        data_tests:
          - accepted_values:
              values: ['Chrome', 'Firefox', 'Safari', 'IE', 'Other'] 
      - name: traffic_source
        description: Traffic source for event information
        data_tests:
          - not_null
          - accepted_values:
              values: ['Adwords', 'Email', 'Facebook', 'Organic', 'YouTube']        
      - name: uri
        description: Event URI
      - name: event_type    
        description: Event type

  - name: stg_looker__inventory_items
    description: Inventory items information
    columns: 
      - name: id
        description: Unique ID for the inventory item
        data_tests:
          - unique
          - not_null
      - name: product_id
        description: Product ID associated with the inventory
        data_tests:
          - not_null
      - name: created_at
        description: Inventory created at timestamp 
        data_tests:
          - not_null
      - name: sold_at
        description: Timestamp inventory item was sold at 
      - name: cost
        description: Cost of the item 
      - name: product_category
        description: Product category
      - name: product_name
        description: Product name
      - name: product_brand
        description: Product brand 
      - name: product_retail_price
        description: Product retail price
      - name: product_department
        description: Product department
      - name: product_sku
        description: Product SKU number
      - name: product_distribution_center_id
        description: Distribution center ID where the product is stored 

  - name: stg_looker__order_items
    description: Order information on the item level
    columns: 
      - name: id
        description: Unique ID for the order item
        data_tests:
          - unique
          - not_null
      - name: order_id
        description: Order ID associated with the order item
      - name: user_id
        description: User ID associated with the order item
      - name: product_id
        description: Product ID associatd with the order item 
      - name: inventory_item_id
        description: Inventory item ID associated with the order item 
      - name: status
        description: Status of the order
      - name: created_at
        description: Order item created at timestamp 
      - name: shipped_at
        description: Order item shipped at timestamp
      - name: delivered_at
        description: Order item delivered at timestamp
      - name: returned_at
        description: Order item returned at timestamp
      - name: sale_price
        description: Order item sale price

  - name: stg_looker__orders
    description: Order information such as status and timestamps
    columns:
      - name: order_id
        description: Unique ID for the order
        data_tests:
          - unique
          - not_null
      - name: user_id
        description: User ID associated with the order
        data_tests:
          - not_null        
      - name: status
        description: Order status 
        data_tests:
          - not_null        
      - name: gender
        description: Gender of the user associated with the order
      - name: created_at
        description: Order created at timestamp
      - name: returned_at
        description: Order returned at timestamp 
      - name: shipped_at
        description: Order shipped at timestamp
      - name: delivered_at
        description: Order delivered at timestamp
      - name: num_of_item    
        description: Number of items ordered
    
  - name: stg_looker__products
    description: Product information such as SKU and category
    columns: 
      - name: id
        description: Unique ID for the product
        data_tests:
          - unique
          - not_null
      - name: cost
        description: Product cost
      - name: category
        description: Product category
      - name: name
        description: Product name 
      - name: brand
        description: Product brand 
      - name: retail_price
        description: Product retail price
      - name: department
        description: Product department
      - name: sku
        description: Product SKU number
      - name: distribution_center_id
        description: Distribution center ID where the product is stored 
        data_tests:
          - relationships:
              to: ref('stg_looker__distribution_centers')
              field: id            

SELECT 
  id,
  name,
  latitude,
  longitude
FROM {{ ref('looker__distribution_centers') }}

SELECT
  id,
  user_id,
  sequence_number,
  session_id,
  created_at,
  ip_address,
  city,
  state,
  postal_code,
  browser,
  traffic_source,
  uri,
  event_type
FROM {{ source('looker_ecommerce', 'events') }}

SELECT
  id,
  product_id,
  created_at,
  sold_at,
  cost,
  product_category,
  product_name,
  product_brand,
  product_retail_price,
  product_department,
  product_sku,
  product_distribution_center_id
FROM {{ source('looker_ecommerce', 'inventory_items') }}

SELECT
  id,
  order_id,
  user_id,
  product_id,
  inventory_item_id,
  status,
  created_at,
  shipped_at,
  delivered_at,
  returned_at,
  sale_price
FROM {{ source('looker_ecommerce', 'order_items') }}

SELECT 
  order_id,
  user_id,
  status,
  gender,
  created_at,
  returned_at,
  shipped_at,
  delivered_at,
  num_of_item
FROM {{ source('looker_ecommerce', 'orders') }}

SELECT
  id,
  cost,
  category,
  name,
  brand,
  retail_price,
  department,
  sku,
  distribution_center_id
FROM {{ source('looker_ecommerce', 'products') }}

SELECT 
  id,
  first_name,
  last_name,
  email,
  age,
  gender,
  state,
  street_address,
  postal_code,
  city,
  country,
  latitude,
  longitude,
  traffic_source,
  created_at
FROM {{ source('looker_ecommerce', 'users') }}

{% snapshot inventory_snapshot %}

{{
    config(
      target_schema='main',
      unique_key='id',
      strategy='timestamp',
      updated_at='created_at' 
    )
}}

SELECT * 
FROM {{ source('looker_ecommerce', 'inventory_items') }}

{% endsnapshot %}


{% snapshot orders_snapshot %}

{{
    config(
      target_schema='main',
      unique_key='order_id',
      strategy='timestamp',
      updated_at='created_at' 
    )
}}

SELECT * 
FROM {{ source('looker_ecommerce', 'orders') }}

{% endsnapshot %}


version: 2

models:
  - name: customers
    description: Data mart for customer-related dimensions and facts, for analytical and reporting purposes
    columns:
      - name: user_id
        description: Unique ID for the user
        data_tests:
          - unique
          - not_null
          - relationships:
              to: ref('stg_looker__users')
              field: id
      - name: customer_first_name
        description: Customer first name
        data_tests:
          - not_null
      - name: customer_last_name
        description: Customer last name
        data_tests:
          - not_null
      - name: customer_country
        description: Customer country
      - name: customer_acquisition_channel
        description: Customer acquisition channel
        data_tests:
          - not_null
          - accepted_values:
              values: ['Display', 'Email', 'Facebook', 'Organic', 'Search']
      - name: total_amount_spent 
        description: Total amount customer has spent
      - name: total_items_purchased
        description: Total items purchased
      - name: first_order_completed_at
        description: When the first order was completed at, if at all
      - name: last_order_completed_at
        description: When the last order was completed at, if at all
      - name: num_orders
        description: Total number of orders placed
      - name: num_orders_shipped
        description: Number of orders shipped
      - name: num_orders_complete
        description: Number of orders completed
      - name: num_orders_processing
        description: Number of orders still processing
      - name: num_orders_cancelled
        description: Number of orders cancelled
      - name: num_orders_returned
        description: Number of orders returned
      - name: num_web_sessions
        description: Number of web sessions

  - name: products
    description: Data mart for product-related dimensions and facts, for analytical and reporting purposes
    columns:
      - name: product_id
        description: Unique ID for the product
        data_tests:
          - unique
          - not_null
          - relationships:
              to: ref('stg_looker__products')
              field: id
      - name: product_name
        description: Product name
      - name: product_category
        description: Product category
        data_tests:
          - not_null
      - name: sales_amount
        description: Total sales amount attributed to this product
      - name: cost_of_goods_sold
        description: Total cost of goods sold attributed to this product
        data_tests:
          - not_null
      - name: profit
        description: Profit, calculated by sales amount minus cost of goods sold


WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = 'Complete'
    GROUP BY 1
)

, orders AS (
    SELECT 
        user_id,
        COUNT(DISTINCT order_id) AS num_orders,
        COUNT(DISTINCT 
            CASE WHEN status = 'Shipped'
            THEN order_id
            END) AS num_orders_shipped,
        COUNT(DISTINCT 
            CASE WHEN status = 'Complete'
            THEN order_id
            END) AS num_orders_complete,
        COUNT(DISTINCT 
            CASE WHEN status = 'Processing'
            THEN order_id
            END) AS num_orders_processing,
        COUNT(DISTINCT 
            CASE WHEN status = 'Cancelled'
            THEN order_id
            END) AS num_orders_cancelled,
        COUNT(DISTINCT 
            CASE WHEN status = 'Returned'
            THEN order_id
            END) AS num_orders_returned,
    FROM {{ ref('stg_looker__orders') }}
    GROUP BY 1
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,
    FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.num_orders,
    o.num_orders_shipped,
    o.num_orders_complete,
    o.num_orders_processing,
    o.num_orders_cancelled,
    o.num_orders_returned,
    wt.num_web_sessions,
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


WITH product_base AS (
    SELECT 
        id AS product_id,
        name AS product_name,
        category AS product_category,
        department AS product_department
    FROM {{ ref('stg_looker__products') }}
)

, inventory_items AS (
    SELECT 
        product_id,
        SUM(CASE WHEN sold_at IS NOT NULL THEN cost END) AS cost_of_goods_sold
    FROM {{ ref('stg_looker__inventory_items') }}
    GROUP BY 1
)

, order_items AS (
    SELECT 
        product_id,
        SUM(sale_price) AS sales_amount
    FROM {{ ref('stg_looker__order_items') }}
    GROUP BY 1    
)

SELECT 
    pb.product_id,
    pb.product_name,
    pb.product_category,
    oi.sales_amount,
    ii.cost_of_goods_sold,
    oi.sales_amount - ii.cost_of_goods_sold AS profit

FROM product_base pb 
LEFT JOIN inventory_items ii 
    ON pb.product_id = ii.product_id 
LEFT JOIN order_items oi 
    ON pb.product_id = oi.product_id


version: 2

models:
  - name: customers
    description: Data mart for customer-related dimensions and facts, for analytical and reporting purposes
    columns:
      - name: user_id
        description: Unique ID for the user
        data_tests:
          - unique
          - not_null
          - relationships:
              to: ref('stg_looker__users')
              field: id
      - name: customer_first_name
        description: Customer first name
        data_tests:
          - not_null
      - name: customer_last_name
        description: Customer last name
        data_tests:
          - not_null
      - name: customer_country
        description: Customer country
      - name: customer_acquisition_channel
        description: Customer acquisition channel
        data_tests:
          - not_null
          - accepted_values:
              values: ['Display', 'Email', 'Facebook', 'Organic', 'Search']
      - name: total_amount_spent 
        description: Total amount customer has spent
      - name: total_items_purchased
        description: Total items purchased
      - name: first_order_completed_at
        description: When the first order was completed at, if at all
      - name: last_order_completed_at
        description: When the last order was completed at, if at all
      - name: num_orders
        description: Total number of orders placed
      - name: num_orders_shipped
        description: Number of orders shipped
      - name: num_orders_complete
        description: Number of orders completed
      - name: num_orders_processing
        description: Number of orders still processing
      - name: num_orders_cancelled
        description: Number of orders cancelled
      - name: num_orders_returned
        description: Number of orders returned
      - name: num_web_sessions
        description: Number of web sessions

  - name: products
    description: Data mart for product-related dimensions and facts, for analytical and reporting purposes
    columns:
      - name: product_id
        description: Unique ID for the product
        data_tests:
          - unique
          - not_null
          - relationships:
              to: ref('stg_looker__products')
              field: id
      - name: product_name
        description: Product name
      - name: product_category
        description: Product category
        data_tests:
          - not_null
      - name: sales_amount
        description: Total sales amount attributed to this product
      - name: cost_of_goods_sold
        description: Total cost of goods sold attributed to this product
      - name: profit
        description: Profit, calculated by sales amount minus cost of goods sold


Opwarmen met dbt compile

version: 2

models:
  - name: customers
    description: Data mart for customer-related dimensions and facts, for analytical and reporting purposes
    columns:
      - name: user_id
        description: Unique ID for the user
        data_tests:
          - unique
          - not_null
          - relationships:
              to: ref('stg_looker__users')
              field: id
      - name: customer_first_name
        description: Customer first name
        data_tests:
          - not_null
      - name: customer_last_name
        description: Customer last name
        data_tests:
          - not_null
      - name: customer_country
        description: Customer country
      - name: customer_acquisition_channel
        description: Customer acquisition channel
        data_tests:
          - not_null
          - accepted_values:
              values: ['Display', 'Email', 'Facebook', 'Organic', 'Search']
      - name: total_amount_spent 
        description: Total amount customer has spent
      - name: total_items_purchased
        description: Total items purchased
      - name: first_order_completed_at
        description: When the first order was completed at, if at all
      - name: last_order_completed_at
        description: When the last order was completed at, if at all
      - name: num_orders
        description: Total number of orders placed
      - name: num_orders_shipped
        description: Number of orders shipped
      - name: num_orders_complete
        description: Number of orders completed
      - name: num_orders_processing
        description: Number of orders still processing
      - name: num_orders_cancelled
        description: Number of orders cancelled
      - name: num_orders_returned
        description: Number of orders returned
      - name: num_web_sessions
        description: Number of web sessions

  - name: products
    description: Data mart for product-related dimensions and facts, for analytical and reporting purposes
    columns:
      - name: product_id
        description: Unique ID for the product
        data_tests:
          - unique
          - not_null
          - relationships:
              to: ref('stg_looker__products')
              field: id
      - name: product_name
        description: Product name
      - name: product_category
        description: Product category
        data_tests:
          - not_null
      - name: sales_amount
        description: Total sales amount attributed to this product
      - name: cost_of_goods_sold
        description: Total cost of goods sold attributed to this product
        data_tests: # Remove not_null data test
          - not_null # Remove not_null data test
      - name: profit
        description: Profit, calculated by sales amount minus cost of goods sold


Opwarmen met dbt compile 2

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM stg_looker__users -- Fix Jinja reference
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = 'Complete'
    GROUP BY 1
)

, orders AS (
    SELECT 
        user_id,
        COUNT(DISTINCT order_id) AS num_orders,
        COUNT(DISTINCT 
            CASE WHEN status = 'Shipped'
            THEN order_id
            END) AS num_orders_shipped,
        COUNT(DISTINCT 
            CASE WHEN status = 'Complete'
            THEN order_id
            END) AS num_orders_complete,
        COUNT(DISTINCT 
            CASE WHEN status = 'Processing'
            THEN order_id
            END) AS num_orders_processing,
        COUNT(DISTINCT 
            CASE WHEN status = 'Cancelled'
            THEN order_id
            END) AS num_orders_cancelled,
        COUNT(DISTINCT 
            CASE WHEN status = 'Returned'
            THEN order_id
            END) AS num_orders_returned,
    FROM stg_looker__orders -- Fix Jinja reference
    GROUP BY 1
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,
    FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.num_orders,
    o.num_orders_shipped,
    o.num_orders_complete,
    o.num_orders_processing,
    o.num_orders_cancelled,
    o.num_orders_returned,
    wt.num_web_sessions,
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


WITH product_base AS (
    SELECT 
        id AS product_id,
        name AS product_name,
        category AS product_category,
        department AS product_department
    FROM {{ ref('stg_looker__products') }} 
)

, inventory_items AS (
    SELECT 
        product_id,
        SUM(CASE WHEN sold_at IS NOT NULL THEN cost END) AS cost_of_goods_sold
    FROM {{ ref('stg_looker__inventory_items') }}
    GROUP BY 1
)

, order_items AS (
    SELECT 
        product_id,
        SUM(sale_price) AS sales_amount
    FROM {{ ref('stg_looker__order_items') }}
    GROUP BY 1    
)

SELECT 
    pb.product_id,
    pb.product_name,
    pb.product_category,
    oi.sales_amount,
    ii.cost_of_goods_sold,
    oi.sales_amount - ii.cost_of_goods_sold AS profit

FROM product_base pb 
LEFT JOIN inventory_items ii 
    ON pb.product_id = ii.product_id 
LEFT JOIN order_items oi 
    ON pb.product_id = oi.product_id


WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }} -- Fix Jinja reference
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = 'Complete'
    GROUP BY 1
)

, orders AS (
    SELECT 
        user_id,
        COUNT(DISTINCT order_id) AS num_orders,
        COUNT(DISTINCT 
            CASE WHEN status = 'Shipped'
            THEN order_id
            END) AS num_orders_shipped,
        COUNT(DISTINCT 
            CASE WHEN status = 'Complete'
            THEN order_id
            END) AS num_orders_complete,
        COUNT(DISTINCT 
            CASE WHEN status = 'Processing'
            THEN order_id
            END) AS num_orders_processing,
        COUNT(DISTINCT 
            CASE WHEN status = 'Cancelled'
            THEN order_id
            END) AS num_orders_cancelled,
        COUNT(DISTINCT 
            CASE WHEN status = 'Returned'
            THEN order_id
            END) AS num_orders_returned,
    FROM {{ ref('stg_looker__orders') }} -- Fix Jinja reference
    GROUP BY 1
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,
    FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.num_orders,
    o.num_orders_shipped,
    o.num_orders_complete,
    o.num_orders_processing,
    o.num_orders_cancelled,
    o.num_orders_returned,
    wt.num_web_sessions,
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


Debuggen met dbt compile

-- Set the value to 'Complete'
{% set order_items_status = ____ %}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
	{# Pass in the variable order_items_status #}
    WHERE status = '{{ ____ }}'
    GROUP BY 1
)

, orders AS (
    SELECT 
        user_id,
        COUNT(DISTINCT order_id) AS num_orders,
        COUNT(DISTINCT 
            CASE WHEN status = 'Shipped'
            THEN order_id
            END) AS num_orders_shipped,
        COUNT(DISTINCT 
            CASE WHEN status = 'Complete'
            THEN order_id
            END) AS num_orders_complete,
        COUNT(DISTINCT 
            CASE WHEN status = 'Processing'
            THEN order_id
            END) AS num_orders_processing,
        COUNT(DISTINCT 
            CASE WHEN status = 'Cancelled'
            THEN order_id
            END) AS num_orders_cancelled,
        COUNT(DISTINCT 
            CASE WHEN status = 'Returned'
            THEN order_id
            END) AS num_orders_returned,
    FROM {{ ref('stg_looker__orders') }}
    GROUP BY 1
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,
    FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.num_orders,
    o.num_orders_shipped,
    o.num_orders_complete,
    o.num_orders_processing,
    o.num_orders_cancelled,
    o.num_orders_returned,
    wt.num_web_sessions,
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


-- Set the value to 'Complete'
{% set order_items_status = 'Complete' %}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
  	{# Pass in the variable order_items_status #}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
    SELECT 
        user_id,
        COUNT(DISTINCT order_id) AS num_orders,
        COUNT(DISTINCT 
            CASE WHEN status = 'Shipped'
            THEN order_id
            END) AS num_orders_shipped,
        COUNT(DISTINCT 
            CASE WHEN status = 'Complete'
            THEN order_id
            END) AS num_orders_complete,
        COUNT(DISTINCT 
            CASE WHEN status = 'Processing'
            THEN order_id
            END) AS num_orders_processing,
        COUNT(DISTINCT 
            CASE WHEN status = 'Cancelled'
            THEN order_id
            END) AS num_orders_cancelled,
        COUNT(DISTINCT 
            CASE WHEN status = 'Returned'
            THEN order_id
            END) AS num_orders_returned,
    FROM {{ ref('stg_looker__orders') }}
    GROUP BY 1
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,
    FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.num_orders,
    o.num_orders_shipped,
    o.num_orders_complete,
    o.num_orders_processing,
    o.num_orders_cancelled,
    o.num_orders_returned,
    wt.num_web_sessions,
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


Variabelen instellen met dbt Jinja

{% set order_items_status = 'Complete' %}
{# Set a Jinja variable called country with value 'Australia' #}
____ 

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
	{# Replace with jinja dynamic filtering #}
    WHERE country = 'Australia' 
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
    SELECT 
        user_id,
        COUNT(DISTINCT order_id) AS num_orders,
        COUNT(DISTINCT 
            CASE WHEN status = 'Shipped'
            THEN order_id
            END) AS num_orders_shipped,
        COUNT(DISTINCT 
            CASE WHEN status = 'Complete'
            THEN order_id
            END) AS num_orders_complete,
        COUNT(DISTINCT 
            CASE WHEN status = 'Processing'
            THEN order_id
            END) AS num_orders_processing,
        COUNT(DISTINCT 
            CASE WHEN status = 'Cancelled'
            THEN order_id
            END) AS num_orders_cancelled,
        COUNT(DISTINCT 
            CASE WHEN status = 'Returned'
            THEN order_id
            END) AS num_orders_returned,
    FROM {{ ref('stg_looker__orders') }}
    GROUP BY 1
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,
    FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.num_orders,
    o.num_orders_shipped,
    o.num_orders_complete,
    o.num_orders_processing,
    o.num_orders_cancelled,
    o.num_orders_returned,
    wt.num_web_sessions,
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


{% set order_items_status = 'Complete' %}
{# Set a Jinja variable called country with value 'Australia' #}
{% set country = 'Australia' %}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
 	{# Replace with jinja dynamic filtering #}
    WHERE country = '{{ country }}'
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
    SELECT 
        user_id,
        COUNT(DISTINCT order_id) AS num_orders,
        COUNT(DISTINCT 
            CASE WHEN status = 'Shipped'
            THEN order_id
            END) AS num_orders_shipped,
        COUNT(DISTINCT 
            CASE WHEN status = 'Complete'
            THEN order_id
            END) AS num_orders_complete,
        COUNT(DISTINCT 
            CASE WHEN status = 'Processing'
            THEN order_id
            END) AS num_orders_processing,
        COUNT(DISTINCT 
            CASE WHEN status = 'Cancelled'
            THEN order_id
            END) AS num_orders_cancelled,
        COUNT(DISTINCT 
            CASE WHEN status = 'Returned'
            THEN order_id
            END) AS num_orders_returned,
    FROM {{ ref('stg_looker__orders') }}
    GROUP BY 1
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,
    FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.num_orders,
    o.num_orders_shipped,
    o.num_orders_complete,
    o.num_orders_processing,
    o.num_orders_cancelled,
    o.num_orders_returned,
    wt.num_web_sessions,
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


Variabelen instellen met dbt Jinja 2

Complexe toepassing van Jinja met dbt

{%- set order_items_status = 'Complete' -%}
{%- set country = 'Australia' -%}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
    WHERE country = '{{ country }}'
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
	-- Replace the following logic with a SELECT * FROM {{ ref('int_orders__pivoted') }}
	SELECT 
        user_id,
        COUNT(DISTINCT order_id) AS num_orders,
        COUNT(DISTINCT CASE WHEN status = 'Shipped' THEN order_id END) AS num_orders_Shipped,
        COUNT(DISTINCT CASE WHEN status = 'Complete' THEN order_id END) AS num_orders_Complete,
        COUNT(DISTINCT CASE WHEN status = 'Processing' THEN order_id END) AS num_orders_Processing,
        COUNT(DISTINCT CASE WHEN status = 'Cancelled' THEN order_id END) AS num_orders_Cancelled,
        COUNT(DISTINCT CASE WHEN status = 'Returned' THEN order_id END) AS num_orders_Returned
  	FROM {{ ref('stg_looker__orders') }}
    -- Remember to remove the GROUP BY
  	GROUP BY 1
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,

        COUNT(DISTINCT CASE WHEN traffic_source = 'Adwords' THEN user_id END) AS num_traffic_source_Adwords,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Email' THEN user_id END) AS num_traffic_source_Email,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Facebook' THEN user_id END) AS num_traffic_source_Facebook,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Organic' THEN user_id END) AS num_traffic_source_Organic,
        COUNT(DISTINCT CASE WHEN traffic_source = 'YouTube' THEN user_id END) AS num_traffic_source_YouTube,

        COUNT(DISTINCT CASE WHEN browser = 'Chrome' THEN user_id END) AS num_browser_Chrome,
        COUNT(DISTINCT CASE WHEN browser = 'Firefox' THEN user_id END) AS num_browser_Firefox,
        COUNT(DISTINCT CASE WHEN browser = 'Safari' THEN user_id END) AS num_browser_Safari,
        COUNT(DISTINCT CASE WHEN browser = 'IE' THEN user_id END) AS num_browser_IE,
        COUNT(DISTINCT CASE WHEN browser = 'Other' THEN user_id END) AS num_browser_Other      
        FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    -- dimensions
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    -- facts 
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.* EXCLUDE(user_id),
    wt.* EXCLUDE(user_id)
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


-- Fill in order status values in line 2
{% set order_statuses = ____ %} 

SELECT 
    user_id,
    COUNT(DISTINCT order_id) AS num_orders,
    -- Fill in lines 7 - 13 for looping through creating orders statuses pivot columns
    {%- ____ order_status ____ order_statuses %}
    COUNT(DISTINCT CASE WHEN status = '{{ order_status }}' THEN order_id END) AS num_orders_{{ order_status }}
    {%- if not loop.last -%}
        ,
    {%- endif -%}
    {%- ____ %}
FROM {{ ref('stg_looker__orders') }}
GROUP BY 1

-- Fill in order status values in line 2
{% set order_statuses = ['Shipped', 'Complete', 'Processing', 'Cancelled', 'Returned'] %} 

SELECT 
    user_id,
    COUNT(DISTINCT order_id) AS num_orders,
    -- Fill in lines 7 - 13 for looping through creating orders statuses pivot columns
    {%- for order_status in order_statuses %}
    COUNT(DISTINCT CASE WHEN status = '{{ order_status }}' THEN order_id END) AS num_orders_{{ order_status }}
    {%- if not loop.last -%}
        ,
    {%- endif -%}
    {%- endfor %}
FROM {{ ref('stg_looker__orders') }}
GROUP BY 1

{%- set order_items_status = 'Complete' -%}
{%- set country = 'Australia' -%}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
    WHERE country = '{{ country }}'
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
	-- Replace the following logic with a SELECT * FROM {{ ref('int_orders__pivoted') }}
    SELECT * 
    FROM {{ ref('int_orders__pivoted') }}
  	-- Remember to remove the GROUP BY
)

, web_traffic AS (
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,

        COUNT(DISTINCT CASE WHEN traffic_source = 'Adwords' THEN user_id END) AS num_traffic_source_Adwords,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Email' THEN user_id END) AS num_traffic_source_Email,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Facebook' THEN user_id END) AS num_traffic_source_Facebook,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Organic' THEN user_id END) AS num_traffic_source_Organic,
        COUNT(DISTINCT CASE WHEN traffic_source = 'YouTube' THEN user_id END) AS num_traffic_source_YouTube,

        COUNT(DISTINCT CASE WHEN browser = 'Chrome' THEN user_id END) AS num_browser_Chrome,
        COUNT(DISTINCT CASE WHEN browser = 'Firefox' THEN user_id END) AS num_browser_Firefox,
        COUNT(DISTINCT CASE WHEN browser = 'Safari' THEN user_id END) AS num_browser_Safari,
        COUNT(DISTINCT CASE WHEN browser = 'IE' THEN user_id END) AS num_browser_IE,
        COUNT(DISTINCT CASE WHEN browser = 'Other' THEN user_id END) AS num_browser_Other      
        FROM {{ ref('stg_looker__events') }}
    GROUP BY 1
)

SELECT
    cb.user_id,
    -- dimensions
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    -- facts 
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.* EXCLUDE(user_id),
    wt.* EXCLUDE(user_id)
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


Jinja gebruiken voor for-loops bij herhaalde SQL 1

{%- set order_items_status = 'Complete' -%}
{%- set country = 'Australia' -%}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
    WHERE country = '{{ country }}'
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
    SELECT * 
    FROM {{ ref('int_orders__pivoted') }}
)

, web_traffic AS (
  	-- Replace the following logic with a SELECT * FROM {{ ref('int_events__pivoted') }}
    SELECT 
        user_id,
        COUNT(DISTINCT session_id) AS num_web_sessions,

        COUNT(DISTINCT CASE WHEN traffic_source = 'Adwords' THEN user_id END) AS num_traffic_source_Adwords,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Email' THEN user_id END) AS num_traffic_source_Email,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Facebook' THEN user_id END) AS num_traffic_source_Facebook,
        COUNT(DISTINCT CASE WHEN traffic_source = 'Organic' THEN user_id END) AS num_traffic_source_Organic,
        COUNT(DISTINCT CASE WHEN traffic_source = 'YouTube' THEN user_id END) AS num_traffic_source_YouTube,

        COUNT(DISTINCT CASE WHEN browser = 'Chrome' THEN user_id END) AS num_browser_Chrome,
        COUNT(DISTINCT CASE WHEN browser = 'Firefox' THEN user_id END) AS num_browser_Firefox,
        COUNT(DISTINCT CASE WHEN browser = 'Safari' THEN user_id END) AS num_browser_Safari,
        COUNT(DISTINCT CASE WHEN browser = 'IE' THEN user_id END) AS num_browser_IE,
        COUNT(DISTINCT CASE WHEN browser = 'Other' THEN user_id END) AS num_browser_Other      
        FROM {{ ref('stg_looker__events') }}
    -- Remember to remove the GROUP BY
    GROUP BY 1
)

SELECT
    cb.user_id,
    -- dimensions
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    -- facts 
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.* EXCLUDE(user_id),
    wt.* EXCLUDE(user_id)
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


{% set traffic_source_values = ['Adwords', 'Email', 'Facebook', 'Organic', 'YouTube'] %}
{% set browser_values = ['Chrome', 'Firefox', 'Safari', 'IE', 'Other'] %}

SELECT 
    user_id,
    COUNT(DISTINCT session_id) AS num_web_sessions,
        
    -- Fill in lines 9 - 11 for looping through traffic source pivot
    {%- for ____ in traffic_source_values %}
    COUNT(DISTINCT CASE WHEN traffic_source = '{{ ____ }}' THEN user_id END) AS num_traffic_source_{{ ____ }},
    {%- endfor %}
      
	-- Fill in lines 14 - 19 for looping through browser pivot
    {%- for ____ in browser_values %}
    COUNT(DISTINCT CASE WHEN browser = '{{ ____ }}' THEN user_id END) AS num_browser_{{ ____ }}
    {%- if not loop.last -%}
        ,
    {%- endif -%}
    {%- ____ %}

FROM {{ ref('stg_looker__events') }}
GROUP BY 1

{% set order_statuses = ['Shipped', 'Complete', 'Processing', 'Cancelled', 'Returned'] %}

SELECT 
    user_id,
    COUNT(DISTINCT order_id) AS num_orders,
    {%- for order_status in order_statuses %}
    COUNT(DISTINCT CASE WHEN status = '{{ order_status }}' THEN order_id END) AS num_orders_{{ order_status }}
    {%- if not loop.last -%}
        ,
    {%- endif -%}
    {%- endfor %}
FROM {{ ref('stg_looker__orders') }}
GROUP BY 1

{% set traffic_source_values = ['Adwords', 'Email', 'Facebook', 'Organic', 'YouTube'] %}
{% set browser_values = ['Chrome', 'Firefox', 'Safari', 'IE', 'Other'] %}

SELECT 
    user_id,
    COUNT(DISTINCT session_id) AS num_web_sessions,
    
    -- Fill in lines 9 - 11 for looping through traffic source pivot    
    {%- for traffic_source_value in traffic_source_values %}
    COUNT(DISTINCT CASE WHEN traffic_source = '{{ traffic_source_value }}' THEN user_id END) AS num_traffic_source_{{ traffic_source_value }},
    {%- endfor %}
    
	-- Fill in lines 14 - 19 for looping through browser pivot
    {%- for browser_value in browser_values %}
    COUNT(DISTINCT CASE WHEN browser = '{{ browser_value }}' THEN user_id END) AS num_browser_{{ browser_value }}
    {%- if not loop.last -%}
        ,
    {%- endif -%}
    {%- endfor %}      

FROM {{ ref('stg_looker__events') }}
GROUP BY 1

{%- set order_items_status = 'Complete' -%}
{%- set country = 'Australia' -%}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
    WHERE country = '{{ country }}'
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
    SELECT * 
    FROM {{ ref('int_orders__pivoted') }}
)

, web_traffic AS (
  	-- Replace the following logic with a SELECT * FROM {{ ref('int_events__pivoted') }}
    SELECT *
  	FROM {{ ref('int_events__pivoted') }}
)

SELECT
    cb.user_id,
    -- dimensions
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    -- facts 
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.* EXCLUDE(user_id),
    wt.* EXCLUDE(user_id)
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


Jinja gebruiken voor lussen op herhaalde SQL 2

version: 2

models:
  - name: customers
    description: Data mart for customer-related dimensions and facts, for analytical and reporting purposes
    columns:
      - name: user_id
        description: Unique ID for the user
        data_tests:
          - unique
          - not_null
          - relationships:
              to: ref('stg_looker__users')
              field: id
      - name: customer_first_name
        description: Customer first name
        data_tests:
          - not_null
      - name: customer_last_name
        description: Customer last name
        data_tests:
          - not_null
      - name: customer_country
        description: Customer country
      - name: customer_acquisition_channel
        description: Customer acquisition channel
        data_tests:
          - not_null
          - accepted_values:
              values: ['Display', 'Email', 'Facebook', 'Organic', 'Search']
      - name: total_amount_spent 
        description: Total amount customer has spent
      - name: total_items_purchased
        description: Total items purchased
      - name: first_order_completed_at
        description: When the first order was completed at, if at all
      - name: last_order_completed_at
        description: When the last order was completed at, if at all
      - name: num_orders
        description: Total number of orders placed
      - name: num_orders_shipped
        description: Number of orders shipped
      - name: num_orders_complete
        description: Number of orders completed
      - name: num_orders_processing
        description: Number of orders still processing
      - name: num_orders_cancelled
        description: Number of orders cancelled
      - name: num_orders_returned
        description: Number of orders returned
      - name: num_web_sessions
        description: Number of web sessions

  - name: products
    description: Data mart for product-related dimensions and facts, for analytical and reporting purposes
    columns:
      - name: product_id
        description: Unique ID for the product
        data_tests:
          - unique
          - not_null
          - relationships:
              to: ref('stg_looker__products')
              field: id
      - name: product_name
        description: Product name
      - name: product_category
        description: Product category
        data_tests:
          - not_null
      - name: sales_amount
        description: Total sales amount attributed to this product
        data_tests:
          - not_null        
      - name: cost_of_goods_sold
        description: Total cost of goods sold attributed to this product
        data_tests:
          - not_null        
      - name: profit
        description: Profit, calculated by sales amount minus cost of goods sold
        data_tests:
          - not_null        


{%- set order_items_status = 'Complete' -%}
{%- set country = 'Australia' -%}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
    WHERE country = '{{ country }}'
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
    SELECT * 
    FROM {{ ref('int_orders__pivoted') }}
)

, web_traffic AS (
    SELECT *
    FROM {{ ref('int_events__pivoted') }}
)

SELECT
    cb.user_id,
    -- dimensions
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    -- facts 
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.* EXCLUDE(user_id),
    wt.* EXCLUDE(user_id)
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


WITH product_base AS (
    SELECT 
        id AS product_id,
        name AS product_name,
        category AS product_category,
        department AS product_department
    FROM {{ ref('stg_looker__products') }}
)

, inventory_items AS (
    SELECT 
        product_id,
        SUM(CASE WHEN sold_at IS NOT NULL THEN cost END) AS cost_of_goods_sold
    FROM {{ ref('stg_looker__inventory_items') }}
    WHERE sold_at IS NULL
    GROUP BY 1
)

, order_items AS (
    SELECT 
        product_id,
        SUM(sale_price) AS sales_amount
    FROM {{ ref('stg_looker__order_items') }}
    GROUP BY 1    
)

SELECT 
    pb.product_id,
    pb.product_name,
    pb.product_category,
    
    {{ coalesce_and_round('oi.sales_amount', 2) }} AS sales_amount,
    
    -- Fill in the coalesce and rounding of the cost of goods sold to 2 decimals
	____ AS cost_of_goods_sold, 
    
    {{ coalesce_and_round('oi.sales_amount', 0) }} - {{ coalesce_and_round('ii.cost_of_goods_sold', 0) }} AS profit

FROM product_base pb 
LEFT JOIN inventory_items ii 
    ON pb.product_id = ii.product_id 
LEFT JOIN order_items oi 
    ON pb.product_id = oi.product_id


{% set traffic_source_values = ['Adwords', 'Email', 'Facebook', 'Organic', 'YouTube'] %}
{% set browser_values = ['Chrome', 'Firefox', 'Safari', 'IE', 'Other'] %}

SELECT 
    user_id,
    COUNT(DISTINCT session_id) AS num_web_sessions,
    
    {#- jinja loop for pivoting out traffic source values -#}
    {%- for traffic_source_value in traffic_source_values %}
    COUNT(DISTINCT CASE WHEN traffic_source = '{{ traffic_source_value }}' THEN user_id END) AS num_traffic_source_{{ traffic_source_value }},
    {%- endfor %}

    {#- jinja loop for pivoting out browser values -#}
    {%- for browser_value in browser_values %}
    COUNT(DISTINCT CASE WHEN browser = '{{ browser_value }}' THEN user_id END) AS num_browser_{{ browser_value }}
    {%- if not loop.last -%}
        ,
    {%- endif -%}
    {%- endfor %}

FROM {{ ref('stg_looker__events') }}
GROUP BY 1

WITH product_base AS (
    SELECT 
        id AS product_id,
        name AS product_name,
        category AS product_category,
        department AS product_department
    FROM {{ ref('stg_looker__products') }}
)

, inventory_items AS (
    SELECT 
        product_id,
        SUM(CASE WHEN sold_at IS NOT NULL THEN cost END) AS cost_of_goods_sold
    FROM {{ ref('stg_looker__inventory_items') }}
    WHERE sold_at IS NULL
    GROUP BY 1
)

, order_items AS (
    SELECT 
        product_id,
        SUM(sale_price) AS sales_amount
    FROM {{ ref('stg_looker__order_items') }}
    GROUP BY 1    
)

SELECT 
    pb.product_id,
    pb.product_name,
    pb.product_category,

    {{ coalesce_and_round('oi.sales_amount', 2) }} AS sales_amount,

    -- Fill in the coalesce and rounding of the cost of goods sold to 2 decimals
    {{ coalesce_and_round('ii.cost_of_goods_sold', 2) }} AS cost_of_goods_sold,
    
    {{ coalesce_and_round('oi.sales_amount', 0) }} - {{ coalesce_and_round('ii.cost_of_goods_sold', 0) }} AS profit

FROM product_base pb 
LEFT JOIN inventory_items ii 
    ON pb.product_id = ii.product_id 
LEFT JOIN order_items oi 
    ON pb.product_id = oi.product_id


-- Copy paste the Jinja macro here
{%- macro coalesce_and_round(column_name, decimal_places = 2) -%}
    ROUND(COALESCE({{ column_name }}, 0), {{ decimal_places }})
{%- endmacro %}

Macro's met Jinja

{# Modify lines 2 and 3 to trim whitespaces #}
{% set order_items_status = 'Complete' %}
{% set country = 'Australia' %}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
    WHERE country = '{{ country }}'
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
    SELECT * 
    FROM {{ ref('int_orders__pivoted') }}
)

, web_traffic AS (
    SELECT *
  	FROM {{ ref('int_events__pivoted') }}
)

SELECT
    cb.user_id,
    -- dimensions
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    -- facts 
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.* EXCLUDE(user_id),
    wt.* EXCLUDE(user_id)
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


SELECT 
    user_id,
    COUNT(DISTINCT session_id) AS num_web_sessions,
    
    {#- jinja loop for pivoting out traffic source values -#}
    {%- for traffic_source_value in traffic_source_values %}
    COUNT(DISTINCT CASE WHEN traffic_source = '{{ traffic_source_value }}' THEN user_id END) AS num_traffic_source_{{ traffic_source_value }},
    {%- endfor %}

    {#- jinja loop for pivoting out browser values -#}
    {%- for browser_value in browser_values %}
    COUNT(DISTINCT CASE WHEN browser = '{{ browser_value }}' THEN user_id END) AS num_browser_{{ browser_value }}
    {%- if not loop.last -%}
        ,
    {%- endif -%}
    {%- endfor %}

FROM {{ ref('stg_looker__events') }}
GROUP BY 1
      

{% set order_statuses = ['Shipped', 'Complete', 'Processing', 'Cancelled', 'Returned'] %}

SELECT 
    user_id,
    COUNT(DISTINCT order_id) AS num_orders,
    {%- for order_status in order_statuses %}
    COUNT(DISTINCT CASE WHEN status = '{{ order_status }}' THEN order_id END) AS num_orders_{{ order_status }}
    {%- if not loop.last -%}
        ,
    {%- endif -%}
    {%- endfor %}
FROM {{ ref('stg_looker__orders') }}
GROUP BY 1


{# Modify lines 2 and 3 to trim whitespaces #}
{%- set order_items_status = 'Complete' -%}
{%- set country = 'Australia' -%}

WITH customer_base AS (
    SELECT 
        id AS user_id,
        first_name AS customer_first_name,
        last_name AS customer_last_name,
        country AS customer_country,
        traffic_source AS customer_acquisition_channel
    FROM {{ ref('stg_looker__users') }}
    WHERE country = '{{ country }}'
)

, order_items AS (
    SELECT 
        user_id,
        SUM(sale_price) AS total_amount_spent,
        COUNT(DISTINCT id) AS total_items_purchased,
        MIN(created_at) AS first_order_completed_at,
        MAX(created_at) AS last_order_completed_at,
    FROM {{ ref('stg_looker__order_items') }}
    WHERE status = '{{ order_items_status }}'
    GROUP BY 1
)

, orders AS (
    SELECT * 
    FROM {{ ref('int_orders__pivoted') }}
)

, web_traffic AS (
    SELECT *
  	FROM {{ ref('int_events__pivoted') }}
)

SELECT
    cb.user_id,
    -- dimensions
    cb.customer_first_name,
    cb.customer_last_name,
    cb.customer_country,
    cb.customer_acquisition_channel,
    -- facts 
    oi.total_amount_spent,
    oi.total_items_purchased,
    oi.first_order_completed_at,
    oi.last_order_completed_at,
    o.* EXCLUDE(user_id),
    wt.* EXCLUDE(user_id)
FROM customer_base cb
LEFT JOIN order_items oi 
    on cb.user_id = oi.user_id
LEFT JOIN orders o
    ON cb.user_id = o.user_id 
LEFT JOIN web_traffic wt 
    ON cb.user_id = wt.user_id


Witruimtes trimmen met Jinja

Samenvatting: dbt-casestudy

Improving dbt with Jinja

Ontgrendel de kracht van datatransformatie met onze praktische dbt (Data Build Tool) casestudiecursus. Ontwikkeld voor beginnende dbt-gebruikers, richt deze cursus zich op realistische, hands-on oefeningen die je meerdere kansen geven om essentiële dbt-opdrachten te bouwen, transformeren en verfijnen. Door zelf aan de slag te gaan, krijg je vertrouwen in elke stap van het proces—van het opzetten van je dbt-omgeving tot het maken van geavanceerde modellen die kritieke zakelijke uitdagingen oplossen. Met interactieve oefeningen bouw je de ervaring op die je nodig hebt om echte ETL-projecten voor data-pijplijnen met vertrouwen aan te pakken.

Zet ruwe data om in nette, betrouwbare modellen met dbt (Data Build Tool) – een modern, op SQL gebaseerd transformatieframework dat door datateams over de hele wereld wordt gebruikt. Deze praktische casestudiecursus is bedoeld voor mensen die net beginnen en via begeleide, praktische oefeningen echte vaardigheden willen opdoen. Je gaat je eigen dbt-omgeving opzetten, gegevens op grote schaal modelleren en herbruikbare code schrijven met de ingebouwde functies van dbt.<br><br><h2>Zet je project op en check de gegevens</h2>Begin met het opzetten van een dbt-project en werk met een echte e-commerce dataset. Je gaat ruwe data structureren, profielen instellen en syntaxproblemen oplossen, terwijl je inzicht krijgt in de zakelijke context achter elke transformatiestap.<br><br><h2>Modellen bouwen en valideren</h2>Leer hoe je schaalbare stagingmodellen kunt maken en datakwaliteitscontroles kunt toepassen om ervoor te zorgen dat je datasets accuraat en klaar voor analyse zijn. Je legt een stevige basis voor het beantwoorden van belangrijke zakelijke vragen.<br><br><h2>Automatiseer met Jinja</h2>Rond de cursus af door te leren hoe je Jinja kunt gebruiken om herbruikbare, onderhoudbare code te schrijven. Je gaat variabelen, controlestromen en loops gebruiken om het DRY-principe (Don't Repeat Yourself) te volgen en je dbt-workflow te stroomlijnen.

Data Manipulation in SQL

Intermediate dbt

Leer hoe je ruwe data omzet in betrouwbare modellen met dbt via praktische oefeningen.

Casestudy: e-commercedatamodellen bouwen met dbt

Leer hoe je ruwe data omzet in nette, betrouwbare modellen met dbt door middel van praktische, realistische oefeningen. 

Macro's met Jinja

Casestudy: e-commercedatamodellen bouwen met dbt

Praktische interactieve oefening