type

project_id

private_key_id

client_email

client_id

auth_uri

token_uri

auth_provider_x509_cert_url

client_x509_cert_url

private_key

project_name

dataset_name

dataset_location

dataproc_cluster_name

gcs_bucket

dataproc_region

dev_dataset_name

Gain insights into setting up dbt, managing models, and leveraging features like data testing. Delve into efficient data pipelines and improve data workflows in this data build tools course.

dbt.tqr.gz

Gcloud authentification

Welcome

Move to usercode

Coding challenge 1

Run shop project

Coding challenge 2-copy

Environment variables

Packages

Dbt tests

Dbt init

Dbt debug & run

Dbt run test_project

Dbt  compile

Dbt run select

dbt snapshot

Dbt  compile analyses

Dbt  docs

Finale coding challenge

dbt Seed

dbt versions

Run both targets

This course offers a comprehensive introduction to data build tools (dbt), a powerful data transformation tool that streamlines the development and management of data pipelines. You will learn to set up and configure dbt projects, create and manage models, work with data sources, and leverage advanced features like data testing, targets, and snapshots.

The course covers various topics, from the basics of dbt to more advanced concepts. You will gain hands-on experience through coding challenges and quizzes, reinforcing your understanding of the material. By the end of the course, you will be well-equipped to build efficient and maintainable data pipelines using dbt.

Whether you are a data engineer, analyst, or data scientist, this course will equip you with the skills and knowledge to leverage dbt to its full potential. You will learn to improve data quality, automate data workflows, and collaborate effectively within your team.

Learn Data Build Tools ( DBT )

```
CREATE VIEW dataset.hello_world AS ( SELECT "Hello World" AS message)
```


```
{{config(dataset="new_dataset")}}
SELECT "Hello World" AS message
```

```
- column: message
  value: "Hello World"
```

**(Select all that apply.)** Which is a valid model file?


(Select all that apply.) Which is a valid model file?

What’s the correct function to select from another model?

What’s the correct function to select from another model?

It is overridden by config in the `dbt_project.yml` file.

It overrides config in the `dbt_project.yml` file.

What is true about adding a config block in the model file?

What is true about adding a config block in the model file?

With the default macro `generate_schema_name`, if a dataset `target_dataset` is specified in the model config and `default_dataset` is specified in `profiles.yml`, in which dataset will dbt create the model?

With the default macro <code>generate_schema_name</code>, if a dataset <code>target_dataset</code> is specified in the model config and <code>default_dataset</code> is specified in <code>profiles.yml</code>, in which dataset will dbt create the model?

They can be used with Python libraries such as numpy and scikit-learn.

There is more documentation on Python models than on SQL models. 

**(Select all that apply.)** Which statement is true about Python models?

(Select all that apply.) Which statement is true about Python models?

Introduction to dbt and Its Setup

Overview of the Project Architecture

Models

Sources

Materializations

Dynamic Models with Jinja

Packages

Advanced dbt Concepts

Conclusion

Quiz: Models