Deep Dive DBT — Chapter 3 — DBT Packages

DBT packages are sets of templates, macros, and other resources that are used to increase DBT’s capability. Packages can be published and installed from dbt Hub, GitHub, or locally by providing the project path. They can also be used to share common code and resources throughout many dbt projects.

A few broad benefits of using packages in DBT are as follows:

· Code can be shared across projects and models thanks to packages. Because you won’t need to copy and paste the same code several times, this can save you a ton of time and work.

· Several people can work on the same model simultaneously when models are packaged into a single package. To ensure the accuracy and dependability of your models, you can use tools like the dbt-test command and version control systems like git to handle model modifications.

· You can distribute templates or macros to other people after packaging them. Others can install and utilize your templates in their dbt projects if you publish your packages on GitHub or dbt Hub.

· By encapsulating models in a package, you may divide the data flow into easier-to-understand and easier-to-maintain smaller, more manageable portions. Over time, it can grow and manage your DBT project. If you are working on a big project with many of different models and transformations, this is quite helpful.

Generally speaking, using packages with dbt can help you build data pipelines that are more effective, scalable, and maintainable.

Here are some of the most widely used dbt packages:

Codegen:
dbt Codegen is a powerful dbt package that automates the creation of dbt models based on your source data and defined schema. It reduces manual effort by generating SQL code for models, making it easier to maintain a scalable and consistent data transformation process. With Codegen, you can quickly scaffold models, ensuring alignment with your data source structure.

Audit Helper:
Audit Helper is a dbt package designed to facilitate data quality and integrity checks. It includes pre-built macros for auditing data consistency, validating key constraints, and ensuring data quality within your dbt project.

dbt-utils:
dbt-utils is a comprehensive package providing a collection of utility functions to enhance your dbt project. It includes functions for string manipulation, date formatting, type casting, and more. dbt-utils simplifies complex transformations and helps maintain consistent coding practices.

dbt-expectations:
dbt-expectations is a package for defining and testing expectations about the data in your dbt project. It allows you to set explicit rules for your data, such as ensuring certain columns are not null or specifying value ranges. This package helps in validating data quality and adherence to defined expectations.

Install dbt Packages:

To use dbt packages, you need to install them. Add the package name and version to your packages.yml file in your dbt project:

packages:
  - package: dbt-labs/dbt_utils
    version: 1.1.1

Run the following command to install the package:

dbt deps

After successful installation packages can be seen in dbt_packages folder in your dbt project.

References:

dbt — Package hub (getdbt.com)

Note:
“I welcome and appreciate any suggestions or recommendations to enhance the content and objectives of this dbt series. Your input contributes to our shared learning journey.”
You can reach out to me on Naman Goel | LinkedIn