dbt with Teradata Vantage

Overview

This tutorial demonstrates how to use dbt (Data Build Tool) with Teradata Vantage. It's based on the original dbt Jaffle Shop tutorial. A couple of models have been adjusted to the SQL dialect supported by Vantage.

Prerequisites

• Access to a Teradata Vantage instance.

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  If you need a test instance of Vantage, you can provision one for free at https://clearscape.teradata.com

• Python 3.7, 3.8, 3.9, 3.10 or 3.11 installed.

Install dbt

1. Clone the tutorial repository and cd into the project directory:

2. Create a new python environment to manage dbt and its dependencies.

   Activate the environment:

   Windows

   Run in Powershell:

   MacOS

   Linux

3. Install dbt-teradata module and its dependencies. The core dbt module is included as a dependency so you don't have to install it separately:

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   dbt dependencies

   dbt-core module was included as a dependency only up to version 1.7.x of dbt-teradata. Starting from dbt-teradata 1.8.0 and above, dbt-core will not be installed as a dependency. Therefore, you need to explicitly install dbt-core in addition to installing dbt-teradata. More information on decoupling dbt adapters from dbt-core can be found here: https://github.com/dbt-labs/dbt-core/discussions/9171

Configure dbt

Configure dbt to connect to your Vantage database. Create profiles.yml file in the location show below.

Windows

Run in Powershell:

MacOS

Linux

Add the following config to profile.yml file. Adjust <host>, <user>, <password> to match your Teradata Vantage instance.

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Database setup

The following dbt profile points to a database called jaffle_shop. If the database doesn't exist on your Teradata Vantage instance, it will be created. You can also change schema value to point to an existing database in your instance.

Now, that we have the profile file in place, we can validate the setup:

If the debug command returned errors, you likely have an issue with the content of profiles.yml.

About the Jaffle Shop warehouse

jaffle_shop is a fictional e-commerce store. This dbt project transforms raw data from an app database into a dimensional model with customer and order data ready for analytics.

The raw data from the app consists of customers, orders, and payments, with the following entity-relationship diagram:

dbt takes these raw data table and builds the following dimensional model, which is more suitable for analytics tools:

Run dbt

Create raw data tables

In real life, we will be getting raw data from platforms like Segment, Stitch, Fivetran or another ETL tool. In our case, we will use dbt's seed functionality to create tables from csv files. The csv files are located in ./data directory. Each csv file will produce one table. dbt will inspect the files and do type inference to decide what data types to use for columns.

You should now see 3 tables in your jaffle_shop database: raw_customers, raw_orders, raw_payments. The tables should be populated with data from the csv files.

Create the dimensional model

Now that we have the raw tables, we can instruct dbt to create the dimensional model:

So what exactly happened here? dbt created additional tables using CREATE TABLE/VIEW FROM SELECT SQL. In the first transformation, dbt took raw tables and built denormalized join tables called customer_orders, order_payments, customer_payments. You will find the definitions of these tables in ./marts/core/intermediate. In the second step, dbt created dim_customers and fct_orders tables. These are the dimensional model tables that we want to expose to our BI tool.

Test the data

dbt applied multiple transformations to our data. How can we ensure that the data in the dimensional model is correct? dbt allows us to define and execute tests against the data. The tests are defined in ./marts/core/schema.yml. The file describes each column in all relationships. Each column can have multiple tests configured under tests key. For example, we expect that fct_orders.order_id column will contain unique, non-null values. To validate that the data in the produced tables satisfies the test conditions run:

Generate documentation

Our model consists of just a few tables. Imagine a scenario where where we have many more sources of data and a much more complex dimensional model. We could also have an intermediate zone between the raw data and the dimensional model that follows the Data Vault 2.0 principles. Would it not be useful, if we had the inputs, transformations and outputs documented somehow? dbt allows us to generate documentation from its configuration files:

This will produce html files in ./target directory.

You can start your own server to browse the documentation. The following command will start a server and open up a browser tab with the docs' landing page:

Summary

This tutorial demonstrated how to use dbt with Teradata Vantage. The sample project takes raw data and produces a dimensional data mart. We used multiple dbt commands to populate tables from csv files (dbt seed), create models (dbt run), test the data (dbt test), and generate and serve model documentation (dbt docs generate, dbt docs serve).

Further reading

• dbt documentation
• dbt-teradata plugin documentation

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