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.

Introduzione al case study su dbt

Verifica l'installazione di dbt

Inizializza il progetto dbt

Configurare il progetto dbt e caricare i dati

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

Carica il distribution center come file seed di dbt

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


Crea il modello di staging del distribution center

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

Carica e crea il modello utenti tramite 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


Altra pratica con le fonti dbt e i modelli di staging

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

Debugga gli errori più comuni

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

Sblocca tutto il potenziale della trasformazione dei dati con il nostro corso pratico su un caso di studio dbt (Data Build Tool). Pensato per chi è agli inizi con dbt, il corso punta su esercizi concreti e realistici che ti offrono molte opportunità per creare, trasformare e affinare la padronanza dei comandi dbt essenziali. Con la pratica diretta, acquisirai sicurezza in ogni fase del processo—dalla configurazione dell’ambiente dbt alla creazione di modelli avanzati che risolvono sfide di business critiche. Grazie ad esercizi interattivi, costruirai l’esperienza necessaria per affrontare con sicurezza progetti ETL di dati reali.

Trasforma i dati grezzi in modelli puliti e affidabili usando dbt (Data Build Tool), un moderno framework di trasformazione basato su SQL che usano i team di dati di tutto il mondo. Questo corso pratico con casi di studio è pensato per chi sta iniziando e vuole imparare cose utili nella vita di tutti i giorni con esercizi pratici guidati. Configurerai il tuo ambiente dbt, modellerai i dati su larga scala e scriverai codice riutilizzabile usando le funzionalità integrate di dbt.<br><br><h2>Prepara il tuo progetto e dai un'occhiata ai dati</h2>Inizia creando un progetto dbt e lavorando con un vero set di dati di e-commerce. Strutturerai i dati grezzi, configurerai i profili e risolverai i problemi di sintassi, mentre capirai meglio il contesto aziendale dietro ogni fase della trasformazione.<br><br><h2>Costruire e verificare modelli</h2>Impara a creare modelli di staging scalabili e ad applicare controlli di qualità dei dati per assicurarti che i tuoi set di dati siano accurati e pronti per l'analisi. Metterai le basi giuste per rispondere alle domande più importanti sul business.<br><br><h2>Automatizza con Jinja</h2>Finisci il corso imparando a usare Jinja per scrivere codice che puoi riutilizzare e mantenere. Userai variabili, flusso di controllo e loop per seguire il principio DRY (Don't Repeat Yourself, non ripeterti) e semplificare il tuo flusso di lavoro dbt.

Data Manipulation in SQL

Intermediate dbt

Impara a trasformare dati grezzi in modelli affidabili con dbt tramite esercizi pratici e reali.

Case Study: creazione di modelli di dati E‑Commerce con dbt

Impara come trasformare i dati grezzi in modelli puliti e affidabili con dbt attraverso esercizi pratici e realistici. 

Altra pratica con le fonti dbt e i modelli di staging

Case Study: creazione di modelli di dati E‑Commerce con dbt

Esercizio pratico interattivo