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.

dbt-documentatie maken en genereren

Opties voor het dbt docs-commando

dbt-documentatieproces

dbt-documentatiestroom

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


dbt-documentatie maken

Jinja-templates

Wat kan 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
  

Verwerk een Jinja-template

Hiërarchische modellen in dbt

Volgorde van modelcreatie

Hiërarchische modelcreatie


-- 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


Hiërarchieën van modellen bijwerken

More on dbt models

Deze cursus maakt je wegwijs in dbt (data build tool), een populair hulpmiddel dat heeft geleid tot analytics engineering voor het maken en onderhouden van extract-, transform- en load-(ELT)pipelines. Je leert datawarehouses bouwen, datamodellering en transformaties uitvoeren en tests ontwerpen om gegevens te valideren. Je gebruikt SQL en shell-opdrachten om allerlei taken met dbt uit te voeren en ontdekt hoe je documentatie voor je warehouse-gebruikers kunt genereren!

dbt, oftewel de data build tool, heeft de datawereld helemaal op zijn kop gezet. Deze cursus laat je kennismaken met de concepten, termen en manieren om dbt te gebruiken om een voorbeeld van een datawarehouse te maken. Je leert wat dbt is, wanneer je het moet gebruiken en wat de beste manieren zijn om datawarehousing te implementeren. Je gaat met echte datasets werken en dingen doen als extractie, laden en transformeren, zoals je dat ook doet in functies als data-analist, data-engineer en analytics-engineer.

Je leert hoe je een datawarehouse helemaal zelf kunt opzetten, data kunt modelleren en transformeren, en tests kunt maken! Deze cursus geeft je een stevige basis om verder te bouwen in je DBT-reis, ongeacht het type datawarehouse dat je wilt implementeren. Krijg meer vertrouwen in hoe en wanneer je dbt kunt gebruiken door oefeningen te doen met SQL en shell-commando's.

Data Warehousing Concepts

Introduction to Shell

Joining Data in SQL

Deze cursus introduceert dbt voor datamodellering, transformaties, testen en documentatie.

Introductie tot dbt

Deze cursus laat je kennismaken met dbt voor het modelleren, transformeren en testen van data en het maken van documentatie.

dbt-basisprincipes

Professionele data-engineer in Python

Voeg documentatie toe aan modellen, tests, enz.

Gebruik `dbt run` om modellen te genereren

Gebruik `dbt docs generate` om de documentatiebestanden te genereren

dbt-documentatiestroom

Introductie tot dbt

Praktische interactieve oefening