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.

Introductie van de dbt-casestudy

Controleer dbt-installatie

Initialiseer het dbt-project

Het dbt-project instellen en data laden

version: 2

seeds:
  - name: looker__distribution_centers



# 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"


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


id,name,latitude,longitude
1,Memphis TN,35.1174,-89.9711
2,Chicago IL,41.8369,-87.6847
3,Houston TX,29.7604,-95.3698
4,Los Angeles CA,34.05,-118.25
5,New Orleans LA,29.95,-90.0667
6,Port Authority of New York/New Jersey NY/NJ,40.634,-73.7834
7,Philadelphia PA,39.95,-75.1667
8,Mobile AL,30.6944,-88.0431
9,Charleston SC,32.7833,-79.9333
10,Savannah GA,32.0167,-81.1167

Laad distribution center als een dbt seed-bestand

version: 2

models:
  - name: _____ # Fill in the model name
    description: _____ # Fill in an informative description of the model


SELECT *
FROM {{ ref('looker__distribution_centers') }}

version: 2

models:
  - name: stg_looker__distribution_centers # Fill in the model name
    description: Distribution center name and location # Fill in an informative description of the model


Maak een stagingmodel voor distributiecentra

version: 2

models:
  - name: stg_looker__distribution_centers
    description: Distribution center name and location    

  - name: ____ # Fill in the staging model name 
    description: Users demographics and location information 

version: 2

sources:
  - name: looker_ecommerce
    tables:
      - name: ____ 	# Fill in the source name

SELECT *
FROM {{ source(____, ____) }} -- Fill in the source 

SELECT *
FROM {{ source('looker_ecommerce', 'users') }} -- Fill in the source 

version: 2

sources:
  - name: looker_ecommerce
    tables:
      - name: users 	# Fill in the source name

version: 2

models:
  - name: stg_looker__distribution_centers
    description: Distribution center name and location    

  - name: stg_looker__users # Fill in the staging model name 
    description: Users demographics and location information

Laad en maak een gebruikersmodel via dbt source

version: 2

models:
  - name: stg_looker__distribution_centers
    description: Distribution center name and location    

  - name: stg_looker__users
    description: Users demographics and location information
    
  - name: stg_looker__events
    description: User event data

  - name: stg_looker__inventory_items
    description: Inventory items information

  - name: stg_looker__order_items
    description: Order information on the item level

  - name: stg_looker__orders
    description: Order information such as status and timestamps
        
    # Fill in for the staging model for products
    

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

version: 2

sources:
  - name: looker_ecommerce
    tables:
      - name: users
      - name: events
      - name: inventory_items
      - name: order_items
      - name: orders
      # Fill in for the dbt source for products
      ____

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

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

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

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

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

version: 2

sources:
  - name: looker_ecommerce
    tables:
      - name: users
      - name: events
      - name: inventory_items
      - name: order_items
      - name: orders
      # Fill in for the dbt source for products
      - name: products

version: 2

models:
  - name: stg_looker__distribution_centers
    description: Distribution center name and location    

  - name: stg_looker__users
    description: Users demographics and location information
    
  - name: stg_looker__events
    description: User event data

  - name: stg_looker__inventory_items
    description: Inventory items information

  - name: stg_looker__order_items
    description: Order information on the item level

  - name: stg_looker__orders
    description: Order information such as status and timestamps
    
  # Fill in for the staging model for products
  - name: stg_looker__products
    description: Product information such as SKU and category


Meer oefenen met dbt-source en stagingmodellen

version: 2

models:
  - name: stg_looker__distribution_centers
    description: Distribution center name and location    

  - name: stg_looker__users
    description: Users demographics and location information
    
  - name: stg_looker__events
    description: User event data

  - name: stg_looker__inventory_items
    description: Inventory items information

  - name: stg_looker__order_items
    description: Order information on the item level

  - name: stg_looker__orders
    description: Order information such as status and timestamps
    
  - name: stg_looker__products
    description: Product information such as SKU and category


SELECT *
FROM {{ source('looker__distribution_centers') }}

version: 2

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

version: 2

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

Foutopsporing bij veelvoorkomende 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.

Introducing Jinja with dbt

Warming up with dbt compile

Warming up with dbt compile 2

Using dbt compile to debug

Setting variables with dbt Jinja

Setting variables with dbt Jinja 2

Complex usage of jinja with dbt

Using Jinja for loops on repeated SQL 1

Using Jinja for loops on repeated SQL 2

Macros with Jinja

Trimming whitespaces with Jinja

Recap: dbt case study

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. 

Foutopsporing bij veelvoorkomende errors

Casestudy: e-commercedatamodellen bouwen met dbt

Praktische interactieve oefening