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.

Pengenalan studi kasus dbt

Periksa instalasi dbt

Inisialisasi proyek dbt

Menyiapkan proyek dbt dan memuat data

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

Muat distribution center sebagai berkas seed 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


Buat model staging 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

Muat dan buat model pengguna melalui 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


Latihan lanjutan dengan sumber dbt dan model 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

Debug kesalahan umum

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

Manfaatkan kekuatan transformasi data dengan Kursus Studi Kasus dbt (Data Build Tool) berbasis praktik. Dirancang untuk pengguna dbt tingkat awal, kursus ini berfokus pada latihan praktis dunia nyata yang memberi Anda banyak kesempatan untuk membangun, mentransformasi, dan memperdalam pemahaman Anda tentang perintah dbt yang esensial. Melalui praktik langsung, Anda akan percaya diri di setiap langkah proses—mulai dari menyiapkan lingkungan dbt hingga membuat model lanjutan yang memecahkan tantangan bisnis kritis. Dengan mengerjakan latihan interaktif, Anda akan membangun pengalaman yang dibutuhkan untuk menangani proyek pipeline ETL data nyata dengan percaya diri.

Ubah data mentah menjadi model yang bersih dan andal menggunakan dbt (Data Build Tool) - sebuah kerangka kerja transformasi berbasis SQL modern yang digunakan oleh tim data di seluruh dunia. Kursus studi kasus praktis ini dirancang untuk pemula yang ingin mengembangkan keterampilan nyata melalui latihan praktis yang dipandu. Anda akan menyiapkan lingkungan dbt Anda sendiri, memodelkan data dalam skala besar, dan menulis kode yang dapat digunakan kembali menggunakan fitur bawaan dbt.<br><br><h2>Atur Proyek Anda dan Jelajahi Data</h2>Mulailah dengan membuat proyek dbt dan bekerja dengan dataset e-commerce yang sebenarnya. Anda akan mengolah data mentah, mengonfigurasi profil, dan memperbaiki masalah sintaksis sambil memahami konteks bisnis di balik setiap langkah transformasi.<br><br><h2>Bangun dan Validasi Model</h2>Pelajari cara membuat model staging yang skalabel dan menerapkan pemeriksaan kualitas data untuk memastikan dataset Anda akurat dan siap untuk analisis. Anda akan membangun dasar yang kokoh untuk menjawab pertanyaan-pertanyaan bisnis yang penting.<br><br><h2>Otomatisasi dengan Jinja</h2>Selesaikan kursus ini dengan mempelajari cara menggunakan Jinja untuk menulis kode yang dapat digunakan kembali dan mudah dipelihara. Anda akan menggunakan variabel, alur kontrol, dan loop untuk mengikuti prinsip DRY (Don't Repeat Yourself) guna mengoptimalkan alur kerja dbt Anda.

Data Manipulation in SQL

Intermediate dbt

Pelajari cara mengubah data mentah menjadi model andal dengan dbt melalui latihan praktis.

Studi Kasus: Membangun Model Data E-Commerce dengan dbt

Pelajari cara mengubah data mentah menjadi model yang bersih dan andal menggunakan dbt melalui latihan praktis yang relevan dengan dunia nyata. 

Debug kesalahan umum

Studi Kasus: Membangun Model Data E-Commerce dengan dbt

Latihan interaktif praktis