Using dbt with Dagster software-defined assets#

In this tutorial, we'll walk you through integrating dbt with Dagster using dbt's example jaffle shop project, the dagster-dbt library, and a DuckDB database.

By the end of this tutorial, you will:

• Understand how dbt models and Dagster software-defined assets (or SDAs) work together
• Set up a dbt project
• Load the dbt models into Dagster as assets
• Create and materialize upstream Dagster assets
• Create and materialize a downstream asset that outputs a plotly chart

dbt models and Dagster software-defined assets#

Dagster’s software-defined assets (SDAs) bear several similarities to dbt models. A software-defined asset contains an asset key, a set of upstream asset keys, and an operation that is responsible for computing the asset from its upstream dependencies. Models defined in a dbt project are similar to Dagster SDAs in that:

• The asset key for a dbt model is (by default) the name of the model.
• The upstream dependencies of a dbt model are defined with ref or source calls within the model's definition.
• The computation required to compute the asset from its upstream dependencies is the SQL within the model's definition.

These similarities make it natural to interact with dbt models as SDAs. Let’s take a look at a dbt model and an SDA, in code:

Here's what's happening in this example:

• The first code block is a dbt model
  • As dbt models are named using file names, this model is named orders
  • The data for this model comes from a dependency named raw_orders
• The second code block is a Dagster asset
  • The asset key corresponds to the name of the dbt model, orders
  • raw_orders is provided as an argument to the asset, defining it as a dependency

Prerequisites#

To complete this tutorial, you'll need:

• To install dbt, Dagster, and Dagit. Run the following to install everything using pip:

  pip install dbt-core dagster dagit
  

  Refer to the dbt and Dagster installation docs for more info.

• To download the tutorial_dbt_dagster Dagster example. We'll walk you through this in the first step. This example includes:

  • dbt's example jaffle shop project. You can follow along using a different dbt project, but you won't be able to use the code examples in this tutorial as-is.

  • A blank template version of the tutorial project, which you can use to follow along with the tutorial.

  • A finished version of the tutorial project, which you can use to check out the final version of the work you'll do in the tutorial.

  • Dependencies for the following libraries:

    • dagster-dbt. This library allows you to integrate dbt with Dagster.

    • dbt-duckdb. This tutorial uses DuckDB as the database backing dbt.

    • dagster-duckdb. This library provides a resource that enables the materialization of Dagster assets as DuckDB tables.

    • dagster-duckdb-pandas. This library allows you to store pandas DataFrames in DuckDB.

    • pandas. This tutorial uses pandas to fetch raw data.

    • plotly. This tutorial uses plotly to create a histogram asset.

Ready to get started?#

When you've fulfilled all the prerequisites for the tutorial, you can get started by setting up the dbt project.