The data transformation tool dbt (data build tool) has gained immense popularity among data professionals for its ability to streamline SQL writing. One of its standout features is macros, which we will delve into in this article. Fear not; these macros are vastly different from the Excel VBA macros that many dread.

For those who are just starting with dbt, the article titled A gentle introduction to dbt outlines the steps for obtaining a cloud version of dbt, setting up a free account, and establishing a connection to a Microsoft Fabric warehouse. Following this, the article Loading Models from Source Data with dbt builds on the initial setup by demonstrating how to define source tables. Its essential to note that while dbt excels in transforming data using SQL, it does not permit loading source tables directly. Thus, it is advisable to read these introductory articles prior to diving deeper.

In any typical data warehouse, a date dimension is a crucial component, as most data analysis involves some aspect of time. Consequently, creating a comprehensive date table is often among the first tasks undertaken in a data warehouse project. Numerous online resources can guide you through crafting an SQL SELECT statement to generate a date table. However, this article aims to elevate that process by utilizing reusable logic through dbt's packages and macros.

Installing a Package into Your dbt Project

Dbt supports a variety of packages that enhance its functionality. Similar to extensions in Visual Studio Code or modules/packages in Python, these packages provide extensible logic to your dbt projects. A rich repository of available packages can be found at =1.3.0", "<1.4.0"] # get latest patch level of specific minor release

After setting up your packages.yml file, navigate to the command line at the bottom of the screen and run the command dbt deps. This command will install the most current versions of all dependencies listed in your packages.yml file.

Once the package is installed, you can utilize its macros in your dbt models. A particularly useful macro within the dbt_utils package is called date_spine. This macro generates lists of dates based on specified start and end dates, as well as a time interval (such as day), functioning similarly to tally tables in SQL Server.

Creating Our First Macro

In dbt, a macro represents a reusable piece of SQL code, serving as one of the tool's most powerful features. It essentially allows users to implement dynamic SQL on steroids. To illustrate this, we will create a new file in the macros folder named my_date_spine.sql. Within this file, we will include the following code, which integrates SQL and Jinja:

{% macro my_date_spine(start_date, end_date) %}SELECT dates = DATEADD(DAY,[value] - 1,{{ start_date }})FROM GENERATE_SERIES(1, DATEDIFF(DAY, {{ start_date }}, {{ end_date }}) + 1, 1){% endmacro %}

This code employs built-in T-SQL functions such as GENERATE_SERIES and DATEDIFF to craft the date list. In the macro definition, the inputs start_date and end_date are specified as parameters, allowing for flexible date range generation.

Returning to the date dimension, the macro can be invoked as follows:

WITH date_spine AS (  {{ my_date_spine(start_date = "CONVERT(DATE, '01/01/2020', 103)", end_date = "DATEADD(YEAR, 5, CONVERT(DATE, GETDATE(), 103))") }})SELECT * FROM date_spine

Upon compiling the code, dbt integrates the SQL from the macro into our model, replacing any parameter references appropriately. This is one of the key benefits of using macros within dbtthey make SQL code reusable without the performance drawbacks often associated with SQL Server user-defined functions.

Previewing the results will yield the desired list of dates for the date dimension, allowing us to finalize our SELECT statement by incorporating date functions to calculate key columns such as year, quarter, month, and week.

Conclusion

In summary, this article has explored how installing packages can significantly enhance the functionality of dbt. These packages grant access to additional macros, which serve as essential tools for reusing SQL functionality. Moreover, if existing packages do not meet specific needs, you can always create your own macros, as demonstrated by our custom date spine implementation.