Learn about the reasons behind dbt's existence and how it's used to create data transformation projects.

Foundations of dbt

Running the dbt command

Introducing dbt

Version of dbt

Getting help

Users of dbt

Creating a dbt project

Initializing a dbt project

Creating a project profile

Welcome to dbt

Get into the true substance of dbt by working with data models and creating SQL based models within dbt. Learn about basic SQL models, define dependencies, update, and troubleshoot various model issues. 

Working with a first project

dbt project workflow

Running a project

What is a dbt model?

Features of a data model

Creating a dbt model

Updating dbt models

Config files

Updating a dbt model

dbt projects and models

Learn how to use Jinja templates to simplify dbt models and define hierarchical dependencies with the `ref` function to ensure correct model execution.

Creare e generare la documentazione di dbt

Opzioni del comando dbt docs

Processo di documentazione dbt

Flusso della documentazione dbt

nyc_yellow_taxi:
  outputs:
    dev:
      type: duckdb
      path: 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: 'taxi_project'
version: '1.0.0'
config-version: 2

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

# 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"]

target-path: "target"  # directory which will store compiled SQL files
clean-targets:         # directories to be removed by `dbt clean`
  - "target"
  - "dbt_packages"


# Configuring models
# Full documentation: https://docs.getdbt.com/docs/configuring-models

# In this example config, we tell dbt to build all models in the example/
# directory as views. These settings can be overridden in the individual model
# files using the `{{ config(...) }}` macro.
models:
  nyc_yellow_taxi:
  # Config indicated by + and applies to all files under models/example/
    taxi_rides:
    +materialized: view


{{ config(materialized='view')}}


with source_data as (
    select * from read_parquet('yellow_tripdata_2023-01-partial.parquet')
)

select * from source_data


version: 2

models:
  - name: taxi_rides_raw
    description: ____
    access: public


Run the following commands in the terminal:

dbt docs generate
dbt docs serve

version: 2

models:
  - name: taxi_rides_raw
    description: Initial import of the NYC Yellow Taxi trip data from Parquet source
    access: public


Creare la documentazione di dbt

Template Jinja

Cosa può fare Jinja?


# 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: 'nyc_yellow_taxi'
version: '1.0.0'
config-version: 2

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

# 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"]

target-path: "target"  # directory which will store compiled SQL files
clean-targets:         # directories to be removed by `dbt clean`
  - "target"
  - "dbt_packages"


# Configuring models
# Full documentation: https://docs.getdbt.com/docs/configuring-models

# In this example config, we tell dbt to build all models in the example/
# directory as views. These settings can be overridden in the individual model
# files using the `{{ config(...) }}` macro.
#models:
#  nyc_yellow_taxi:
#  # Config indicated by + and applies to all files under models/example/
#    taxi_rides:
#    +materialized: view
    



select * from 'yellow_tripdata_2023-01-partial.parquet'



select
{% for column_name in ['fare_amount', 'tip_amount', 'tolls_amount', 'total_amount'] %}
  {{column_name}}{% if not loop.last %},{% endif %}
{% endfor %}
from taxi_rides_raw
   

Run the following commands in the terminal:
  
  dbt run
  

Elaborare un template Jinja

Modelli gerarchici in dbt

Ordine di creazione dei modelli

Creazione di modelli gerarchici


-- Update SQL to use Jinja reference
select 
   date_part('day', tpep_pickup_datetime) as day,
   count(*) as total_riders
-- Update the line below to use a Jinja function
from taxi_rides_raw
where payment_type = 1
group by day


#!/usr/bin/env python3
import duckdb
con = duckdb.connect('dbt.duckdb', read_only=True)
print(con.sql('select * from creditcard_riders_by_day'))
if (con.execute('select * from creditcard_riders_by_day').fetchall()[0][1] == 54743):
  with open('/home/repl/workspace/successful_data_check', 'w') as f:
    f.write('54743')
    


Run the following commands in the terminal:
dbt run
dbt run -f



# 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: 'nyc_yellow_taxi'
version: '1.0.0'
config-version: 2

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

# 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"]

target-path: "target"  # directory which will store compiled SQL files
clean-targets:         # directories to be removed by `dbt clean`
  - "target"
  - "dbt_packages"


# Configuring models
# Full documentation: https://docs.getdbt.com/docs/configuring-models

# In this example config, we tell dbt to build all models in the example/
# directory as views. These settings can be overridden in the individual model
# files using the `{{ config(...) }}` macro.
models:
  nyc_yellow_taxi:
  # Config indicated by + and applies to all files under models/example/
    taxi_rides:
    +materialized: view



-- Update SQL to use Jinja reference
select 
   date_part('day', tpep_pickup_datetime) as day,
   count(*) as total_riders
-- Update the line below to use a Jinja function
from {{ ref('taxi_rides_raw') }}
where payment_type = 1
group by day


Aggiornare le gerarchie dei modelli

More on dbt models

Questo corso ti introduce a dbt (data build tool), uno strumento molto diffuso che ha dato impulso all'analytics engineering per la creazione e la manutenzione di pipeline di extract, transform, and load (ELT). Imparerai a costruire data warehouse, eseguire data modeling e trasformazioni, e progettare test per la validazione dei dati. Userai SQL e comandi da shell per svolgere diverse attività con dbt e scoprirai come generare documentazione per gli utenti del tuo warehouse!

Il dbt, o data build tool, ha preso d'assalto il mondo dei dati. Questo corso ti spiega i concetti, i termini e i metodi per usare dbt per creare un data warehouse di esempio. Capirai cos'è il dbt, quando è meglio usarlo e quali sono le migliori pratiche per implementare il data warehousing. Lavorerai con dati reali e farai operazioni di estrazione, caricamento e trasformazione, proprio come si fa nei ruoli di analista dati, ingegnere dati e ingegnere analitico.

Gli studenti impareranno a creare un data warehouse da zero, modellare e trasformare i dati e fare dei test! Questo corso ti darà una base solida su cui costruire il tuo percorso con DBT, indipendentemente dal tipo di data warehouse che vuoi implementare. Acquisisci sicurezza su come e quando usare dbt facendo esercizi con SQL e comandi shell.

Data Warehousing Concepts

Introduction to Shell

Joining Data in SQL

Questo corso introduce dbt per modellazione dati, trasformazioni, test e creazione documentazione.

Introduzione a dbt

Questo corso ti spiega cos'è il DBT per modellare i dati, trasformarli, testarli e creare la documentazione.

Fondamenti del dbt

Ingegnere dei dati professionale in Python

Aggiungi la documentazione a modelli, test, ecc.

Usa `dbt docs generate` per generare i file di documentazione

Copia i contenuti nel servizio di hosting

Accedi alla documentazione dal browser web

Flusso della documentazione dbt

Introduzione a dbt

Esercizio pratico interattivo