project and environment variables in dbt

Project variables in dbt allow you to define reusable values across your project. These can range from database connection details to thresholds for data quality checks.

Use Cases:
Connection Strings:

Store database connection strings centrally as variables, ensuring consistency across your project.

Thresholds and Limits:

Define threshold values for data quality checks or other operational parameters.

Example :

# dbt_project.yml
vars:
database_connection: 'my_database_connection_string'
max_rows_threshold: 100000

Packages:

Definition:

Packages in dbt are a way to organize and share modular pieces of dbt functionality, such as models, tests, or macros. They enable code reuse across projects.

Use Cases:

Reusable Logic:

Create packages for common business logic, data transformations, or domain-specific calculations.

Collaboration:

Share packages across teams or open-source communities, fostering collaboration and standardization.

Example:

# dbt_project.yml
packages:
  - git: 'https://github.com/dbt-labs/awesome-analytics.git'
  - local: './my_local_package'

Hooks:

Definition:

Hooks in dbt are functions that run at specific points in the dbt lifecycle, such as before or after model execution. They allow you to perform custom actions or validations.

Use Cases:

Pre- or Post-processing:

Execute tasks before or after dbt model runs, such as loading data into a temporary table.

Custom Logging:

Implement custom logging or notifications based on the success or failure of dbt runs.

Example:

-- my_hook.sql
{% if target.name == 'prod' %}
  {%- set slack_channel = '#production-alerts' %}
{% else %}
  {%- set slack_channel = '#development-alerts' %}
{% endif %}

{% if execute %}
  {%- set status = 'Success' %}
{% else %}
  {%- set status = 'Failure' %}
{% endif %}

{% set message = 'Dbt run completed with status: ' ~ status %}
{% set slack_message = {'channel': slack_channel, 'text': message} %}

{% if target.name == 'prod' %}
  {% do slack_notify(slack_message) %}
{% endif %}

Operations:

Definition:

Operations in dbt are a way to define custom command-line actions. These can include running specific models, tests, or any other task within the dbt framework.

Use Cases:

Custom dbt Commands:

Extend dbt’s functionality with custom commands tailored to your project’s needs.

Automation:

Automate repetitive tasks or orchestrate complex workflows using custom operations.

Example:

# dbt_project.yml
on-run-start:
  - "{{ custom_run_op('my_custom_operation') }}"

We at Helical have more than 10 years of experience in providing solutions and services in the domain of data and have served more than 85+ clients. We are also DBT partners, hence in case if you are looking for certain assistance, consulting, services please do reach out on nikhilesh@Helicaltech.com

Thank You
Nikitha Rastapuram
Helical IT Solutions