Writing SQL in dbt: Best practices and tips

Are you tired of writing SQL queries that are hard to maintain and debug? Do you want to learn how to write SQL in a more structured and organized way? Look no further than dbt!

dbt (data build tool) is a popular open-source tool for managing data transformation pipelines. It allows you to write SQL code in a modular and reusable way, making it easier to maintain and collaborate on your data projects.

In this article, we'll cover some best practices and tips for writing SQL in dbt. Whether you're a beginner or an experienced SQL developer, you'll find something useful here.

Use dbt's modular structure

One of the key benefits of dbt is its modular structure. Instead of writing long and complex SQL queries, you can break them down into smaller, reusable pieces called "models".

A model is a SQL query that defines a table or view in your data warehouse. It can reference other models, allowing you to build up complex data pipelines in a modular way.

Here's an example of a simple model in dbt:

-- models/my_table.sql

select
  column1,
  column2,
  column3
from
  my_source_table

This model selects three columns from a source table and creates a new table called "my_table". You can reference this model in other models by using the ref() function:

-- models/my_analysis.sql

select
  column1,
  count(*) as count
from
  {{ ref('my_table') }}
group by
  column1

This model references the "my_table" model and performs an aggregation on one of its columns. By breaking down your SQL queries into smaller models, you can make them more modular and easier to maintain.

Use dbt's macros

Another powerful feature of dbt is its macro system. A macro is a reusable piece of SQL code that can be called from other SQL queries.

dbt comes with a set of built-in macros that you can use out of the box. For example, the source() macro allows you to define a source table or view in your data warehouse:

-- models/my_source.sql

{{ source('my_database', 'my_source_table') }}

This macro creates a table called "my_source_table" in the "my_database" schema. You can then reference this table in other models using the ref() function.

You can also define your own macros in dbt. For example, you might define a macro that calculates the average of a column:

-- macros/average.sql

{% macro average(column) %}
select
  avg({{ column }})
{% endmacro %}

This macro takes a column name as an argument and returns a SQL query that calculates the average of that column. You can then call this macro from other SQL queries:

-- models/my_analysis.sql

select
  column1,
  {{ average('column2') }} as avg_column2
from
  {{ ref('my_table') }}
group by
  column1

This model uses the average() macro to calculate the average of the "column2" column in the "my_table" model. By using macros, you can make your SQL code more modular and reusable.

Use dbt's testing framework

Testing your SQL code is an important part of data pipeline development. dbt comes with a built-in testing framework that allows you to write tests for your SQL queries.

A test in dbt is a SQL query that checks the output of a model against an expected result. For example, you might write a test that checks that a model returns the correct number of rows:

-- tests/my_table_test.sql

select
  count(*) as row_count
from
  {{ ref('my_table') }}
where
  column1 is not null

This test checks that the "my_table" model returns the correct number of rows where the "column1" column is not null. You can run this test using the dbt test command.

dbt also comes with a set of built-in tests that you can use out of the box. For example, the unique test checks that a column or set of columns in a model contains unique values:

-- models/my_table.sql

select
  column1,
  column2,
  column3
from
  my_source_table
{% if is_incremental() %}
where
  updated_at >= '{{ yesterday }}'
{% endif %}

-- tests/my_table_test.sql

{% if is_incremental() %}
select
  count(*) as row_count
from
  {{ ref('my_table') }}
where
  updated_at >= '{{ yesterday }}'
{% else %}
select
  count(*) as row_count
from
  {{ ref('my_table') }}
{% endif %}

{% if is_incremental() %}
{{ unique('column1', 'column2', 'column3') }}
{% else %}
{{ unique('column1', 'column2', 'column3') }}
{% endif %}

This model and test use the unique test to check that the "column1", "column2", and "column3" columns in the "my_table" model contain unique values. The model also includes an optional is_incremental() function that checks whether the model is incremental or not.

By using dbt's testing framework, you can ensure that your SQL code is correct and reliable.

Use dbt's documentation system

Documenting your SQL code is important for making it understandable and maintainable. dbt comes with a built-in documentation system that allows you to document your models and macros.

To document a model or macro in dbt, you can add a YAML file with the same name as the model or macro. For example, to document the "my_table" model, you might create a file called "my_table.yml":

# models/my_table.yml

description: >
  This model selects three columns from the "my_source_table" table and creates a new table called "my_table".

This YAML file includes a description of the "my_table" model. You can then generate documentation for your dbt project using the dbt docs generate command.

dbt also allows you to add custom documentation tags to your models and macros. For example, you might add a tag that links to a JIRA ticket:

# models/my_table.yml

description: >
  This model selects three columns from the "my_source_table" table and creates a new table called "my_table".

tags:
  - jira:PROJ-1234

This YAML file includes a tag that links to the "PROJ-1234" JIRA ticket. You can then generate documentation that includes these tags using the dbt docs generate command.

By using dbt's documentation system, you can make your SQL code more understandable and maintainable.

Conclusion

In this article, we've covered some best practices and tips for writing SQL in dbt. By using dbt's modular structure, macro system, testing framework, and documentation system, you can write SQL code that is more structured, organized, and maintainable.

If you're new to dbt, we recommend checking out the dbt documentation and tutorials to learn more. And if you're already using dbt, we hope these tips will help you write better SQL code and build more reliable data pipelines. Happy coding!

Editor Recommended Sites