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 source marketing
AS (raw_ads)
```

```
{{ source(
 name="marketing",
 table="raw_ads"
)
}}
```

```
version: 2

sources:
  - name: marketing
    tables:
      - name: raw_ads
```

```
dbt create_source -d marketing -t raw_ads
```

```
SELECT *
FROM {{ ref("source_name") }}
```

```
SELECT *
FROM source_dataset_name.source_table_name
```

```
SELECT *
FROM {{ source("source_name") }}
```

```
SELECT *
FROM {{ source.source_name }}
```

Which command allows us to load a CSV file?

Which command allows us to load a CSV file?

If their location changes, it only has to be changed in one place.

Sources can point to a different table based on the environment. 

Using sources is faster than hardcoding the name of the source table.

Sources give us information on how the table is generated.

**(Select all that apply.)** What are some benefits of using sources?

(Select all that apply.) What are some benefits of using sources?

Models are materialized by a `dbt run` command. Sources are only references to a table.

We can use a YAML file to store info about sources.

Models are referenced using the `ref` function, whereas sources are referenced using the `source` function.

Sources are a dbt best practice, whereas models are not recommended.

**(Select all that apply.)** What are some differences between a source and a model?

(Select all that apply.) What are some differences between a source and a model?

Introduction to dbt and Its Setup

Overview of the Project Architecture

Models

Sources

Materializations

Dynamic Models with Jinja

Packages

Advanced dbt Concepts

Conclusion

Quiz: Sources