Introduction to dbt - Olist E-Commerce Project

A hands-on dbt training project using the Olist Brazilian E-commerce dataset.

Repo: https://github.com/cbfacademy/Introduction-to-DBT

Getting Started

1. Clone this repo:

   git clone https://github.com/cbfacademy/Introduction-to-DBT.git
   cd Introduction-to-DBT

2. Follow Lab 1 instructions in labs/lab_01_setup/README.md

3. Work through each lab in order — each lab folder contains:

   • A README.md with step-by-step instructions
   • The actual .sql and .yml files to copy into the correct locations

Lab Structure

Lab	Folder	What You'll Build
1	labs/lab_01_setup/	BigQuery + dbt setup and connection
2	labs/lab_02_source_ref/	Staging models, sources, ref(), materializations
3	labs/lab_03_jinja_macros/	Jinja loops, custom macros, dbt packages
4	labs/lab_04_marts_testing/	Dimension/fact tables, schema & singular tests
5	labs/lab_05_docs_freshness/	Documentation, source freshness

How to Use the Lab Files

Each lab folder has the SQL/YAML files you need. You can either:

• Follow the README and type/paste the code yourself (recommended for learning)
• Copy the files from the lab folder to the correct location, e.g.:

  cp labs/lab_02_source_ref/stg_orders.sql models/staging/

Final Project Structure (after all labs)

models/
├── staging/           # Clean raw data (views)
│   ├── _sources.yml
│   ├── _staging.yml
│   ├── stg_orders.sql
│   ├── stg_customers.sql
│   ├── stg_order_items.sql
│   ├── stg_products.sql
│   └── stg_product_categories.sql
├── intermediate/      # Business logic (views)
│   ├── int_orders_enriched.sql
│   ├── int_order_items_enriched.sql
│   └── int_orders_by_status.sql
└── marts/             # Final tables for BI
    ├── _marts.yml
    ├── dim_customers.sql
    ├── fct_orders.sql
    └── fct_daily_revenue.sql
macros/
├── limit_in_dev.sql
└── cents_to_currency.sql
tests/
├── assert_positive_prices.sql
└── assert_orders_have_items.sql

Data

The data/ folder contains 5 CSV files from the Olist dataset:

Table	Rows
customers	10,000
orders	10,000
order_items	11,253
products	6,124
product_category_name_translation	71

Prerequisites

• Python 3.8+
• Google Cloud account with BigQuery enabled
• dbt-bigquery (pip install dbt-bigquery)