How to Use DBT to Build a Data Warehouse from Scratch

If you're looking to build a data warehouse from scratch, then DBT (Data Build Tool) is something that you should definitely look at. DBT is an open-source SQL-based building tool, which is great for transforming data using SQL or Python. It's taken the world of data engineering by storm, thanks to its simple yet powerful architecture, which makes it much easier to work with than some other traditional data tools that can be difficult to navigate.

In this article, we're going to give you a step-by-step guide on how to use DBT to build a data warehouse from scratch that will manage to handle large amounts of data easily. We’ll cover some of the basic concepts and terminology you’ll need to know when working with DBT, and then we’ll dive into the details of how to create a data warehouse.

So let's get started.

What is a data warehouse?

A data warehouse is a large collection of data that is used by businesses to make informed decisions. It is used to store data from multiple sources so that it can be analyzed, transformed, and combined to make it more useful.

Data warehouses are designed to help businesses make sense of their data by providing a central location for storage and management. Business intelligence tools are then used to extract insights from the data, which help businesses make informed decisions.

What is DBT?

DBT is a data transformation tool that makes it easy to create repeatable analytics workflows. It helps data teams to build data pipelines that collect data from different sources, transform it, and then load it into a data warehouse. DBT is a pipeline tool that follows a basic development workflow such as testing, building, and deploying.

Step-by-step guide to building a data warehouse with DBT

Here's a step-by-step guide on how to use DBT to build a data warehouse from scratch.

Step 1: Install DBT

Before we start working with DBT, you need to install it in your system. The installation process is straightforward, and you can follow the instructions provided in the official DBT documentation. The documentation will provide information on how to install DBT in your system successfully.

Step 2: Create a DBT project

Once you have installed DBT, you need to create a project in which you'll build your data warehouse. Creating a project is a crucial step as it will set up the default directory structure of your DBT project, which helps in organizing your code correctly.

To create a new project, run the following command:

dbt init my_project

This command will initialize a new DBT project with the name "my_project". Once you run this command, DBT will create a directory structure that looks like this:


Step 3: Create your data model

The next step is to create your data model, which is a set of SQL queries that you'll use to extract data from various sources, transform it, and then load it into your data warehouse. Data models are the building blocks of your data warehouse, and you should spend some time designing them to ensure they work efficiently.

To create a data model, you need to create a new SQL file in the models/ directory. You can give the file any name you like, but it's best to give it a descriptive name that explains what the model does.

Once you've created the SQL file, you can start designing your data model by writing SQL queries. You can create as many data models as you need, depending on the complexity of your data warehouse.

Step 4: Run your data model

Once you have created your data model, you can run it using the following command:

dbt run

This command will execute your data model and load the data into your data warehouse.

Step 5: Test your data model

Testing your data model is essential to ensure it performs as expected, produces the correct results, and, more importantly, doesn't break or corrupt data in the data warehouse.

To test your data model, you need to create a test/ directory and add test files that run SQL queries to test the data you want to validate.

Run the following command to test your data model:

dbt test

This command will run your test models and give you a summary of the tests that failed.

Step 6: Document your data model

Once you have created the data model and tested it, you need to document it so that others in your team can understand it. Documentation is especially important when you're working with a large team because it keeps everyone on the same page.

To document your data model, you can create a doc/ directory and add markdown files that explain how your data model works, what it does, and what its output is.

Step 7: Deploy your data model

Once you have thoroughly tested your data model, it's time to deploy it into your data warehouse. Deployment is important because it ensures the data model works correctly and produces the desired results.

To deploy, you can run the following command:

dbt deploy

This command will deploy your data model to your data warehouse.

Step 8: Schedule your data model

Finally, scheduling your data model ensures it runs automatically, saving you time and effort. You can schedule your data model using the cron syntax. The cron syntax is used to define the frequency of running your model.

For example, if you want to run your data model once a day at 12 PM, you can use the following command:

dbt schedule create my_daily_schedule --cron '0 12 * * *'

This command will create a new schedule called my_daily_schedule that runs your data model every day at 12 PM.


In this article, we covered the basics of building a data warehouse using DBT. We learned what a data warehouse is, what DBT is, and how to install and use DBT to build a data warehouse from scratch.

Building a data warehouse requires some planning and thought, but with DBT, you can create a reliable and scalable data warehouse without breaking a sweat!

Editor Recommended Sites

AI and Tech News
Best Online AI Courses
Classic Writing Analysis
Tears of the Kingdom Roleplay
Prompt Engineering Jobs Board: Jobs for prompt engineers or engineers with a specialty in large language model LLMs
Business Process Model and Notation - BPMN Tutorials & BPMN Training Videos: Learn how to notate your business and developer processes in a standardized way
Database Migration - CDC resources for Oracle, Postgresql, MSQL, Bigquery, Redshift: Resources for migration of different SQL databases on-prem or multi cloud
Explainable AI: AI and ML explanability. Large language model LLMs explanability and handling
Cloud Monitoring - GCP Cloud Monitoring Solutions & Templates and terraform for Cloud Monitoring: Monitor your cloud infrastructure with our helpful guides, tutorials, training and videos