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

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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 )


**(Select all that apply.)** Which materializations store the data?

(Select all that apply.) Which materializations store the data?

They are a good option to reduce query costs.

It's a good idea to use tables to connect them directly with BI tools.


**(Select all that apply.)** What is true about tables?

(Select all that apply.) What is true about tables?

They proceed to a scan of the whole table before updating it.

They can avoid duplicates when a unique key is specified.

They are more expensive than those with table materialization.


**(Select all that apply.)** What is true about incremental models with the `MERGE` strategy?

(Select all that apply.) What is true about incremental models with the <code>MERGE</code> strategy?

They don't proceed to a full scan of the table every time.

They drop the table every time the model is run.

They are the easiest materialization to implement.

**(Select all that apply.)** Which statement is correct about incremental models with the `insert_overwrite` strategy?

(Select all that apply.) Which statement is correct about incremental models with the <code>insert_overwrite</code> strategy?

They are a good way to simplify our CTEs (Common Table Expressions).

They are a good solution to store very complex transformations.

**(Select all that apply.)** Which statement is correct about ephemeral models?

(Select all that apply.) Which statement is correct about ephemeral models?

Test your knowledge of materializations in dbt.

Introduction to dbt and Its Setup

Overview of the Project Architecture

Models

Sources

Materializations

Dynamic Models with Jinja

Packages

Advanced dbt Concepts

Conclusion

Quiz: Materializations