dbt Data Modeling: A Comprehensive Guide 101

Manjiri Gaikwad • Last Modified: March 31st, 2023

dbt Data Modeling FI

Big data has become a differentiator for organizations, allowing decision-makers to boost business growth. However, to leverage the potential of the collected data, organizations have to build a better relationship among the datasets. 

This allows data professionals to understand the business requirement and pull the necessary data to generate insights. They use several techniques and tools to enhance the data modeling processes in organizations. One of the popular tools for data modeling is dbt. It helps with transforming and modeling data within a data warehouse. But let’s understand what data modeling is before we jump into dbt data modeling.

What is Data Modeling?

dbt Data Modeling: image 1
Image Source

Data modeling is about creating a visual representation of an information system by applying connections between data points. 

One of the famous examples of data modeling is arranging data into tables. Tables consist of rows and columns that enable relationships between data points quickly. 

The process of data modeling is carried out in businesses to obtain high-quality, consistent, and structured data for running business applications. To create data models, you must embrace modularity and define relationships between the collected data.

Data Modeling Techniques

There are several techniques for implementing data modeling. Some of them are as follows: 

  • Relational Data Model 
dbt Data Modeling: image 2
Image Source

In the relational data model, data is stored in tables, and their relationship is identified. It also consists of features like triggers and constraints. The relational model became the dominant data modeling technique in the 1980s. The entity-relationship and dimensional data models are variations of the relational data model.

  • Hierarchical Data Model 
dbt Data Modeling: image 3
Image Source

The hierarchical data model technique consists of a tree-like structure. It contains one root node or a parent node and child nodes. The parent node can have one or more child nodes, but the child node can only have one parent node. The hierarchical model is used very rarely nowadays. It was developed in the 1960s in the mainframe databases.

  • Network Data Model 
dbt Data Modeling: image 4
Image Source

The network model extends the hierarchical data model where every child node can have one or more parent nodes. With a network data model, businesses can represent objects and their relationships flexibly. 

In the network model, there is a feature for defining a schema as a graph. An object is represented inside a node in the network model, and the relation between two nodes is an edge. Due to edges, it is easier to maintain multiple parent and child records in a generalized manner.

What is dbt Data Modeling?

dbt is a framework used for high-level SQL transformation logic. Data engineers use dbt to transform and model data in a data warehouse. It is an essential tool used in data modeling to build the data model into smaller modules. With dbt, you can modularize data transformation logic into discrete dbt models. It enables you to reuse the dbt models for different use cases to enhance productivity and collaboration. dbt data modeling is also beneficial for testing and debugging data models.

Implementing Data Modeling with dbt

Data modeling with dbt is a way of transforming data for business intelligence or downstream applications in a modular approach. The transformation logic is built through dbt models consisting of SQL SELECT statements. 

These models can then be referenced by other models to obtain modularity in your dbt projects. dbt also includes dbt sources which are metadata for the raw data. 

Using sources, you can reference the underlying data to build transformation in dbt models. The data defined in the dbt sources can be referenced in the dbt models using the source function. The approach of referencing transformation logic and data sources in every step of the dbt workflows makes it a good fit for data modeling.

The ability to include test cases to establish data quality and integrity while building a dbt project further helps in data modeling. Tests are SQL expression that validates models, sources, and other checkpoints. 

To start with dbt data modeling, you need to know the data source, what you want to create (views or tables), testing requirements, and more. 

In a dbt project, there are two core files: .config and .sql. The .config files define sources, configure paths, and set versions. It informs dbt how the data models can be built in the target environment. 

.sql files are used to define your data models. It consists of a configuration block that uses Jinja, common table expressions, and other temporary tables. .sql files also contain a final select statement that gives you the transformed data.

Consider the following command:

select * from {{ ref(‘stg_customers’) }}

The double curly brackets in the aforementioned command are jinja syntax that tells dbt to reference another dataset within its data source. In this case, it is ‘stg_customers.’

Deploying and Scaling dbt Data Models

After understanding dbt data modeling, let’s learn more about deploying and scaling dbt data models.

Deploying dbt Models

In this section, you will learn to deploy dbt models within Snowflake. It is assumed that you have created your dbt project and first data model in dbt in Snowflake. After creating models, you need to deploy your dbt models from the development environment to the production environment. You can read more about it in the dbt documentation

Follow the steps mentioned below to deploy your dbt data models:

  • Ensure that you have committed all your work to the feature branch. 
  • After your work is committed, you can see the git workflow button “Merge to main.” Click on the ‘Merge to main’ to start the merging process in the background automatically.
dbt Data Modeling: image 5
Image Source
  • Once the merging is finished, you can see the git button “Create Branch.”
  • Your development work is merged into the main branch. Now, you can build your deployment job. But you need to update some configurations according to your needs.
  • Go to Deploy > Environments from the menu.
dbt Data Modeling: image 6
Image Source
  • Select the Deployment environment and Settings to modify it.
  • You should check what is defined in the environment before making any changes to it. The Snowflake connection shows the credentials that dbt Cloud is using for the environment. The deployment job will be built in the PC_DBT_DB database.
  • Create a new schema for the production environment. Navigate to Credentials > Schema. Click on Edit to modify the existing field values. 
  • Change the schema name to production. Select Save after making changes.
dbt Data Modeling: image 7
Image Source
  • After updating the schema, your deployment job will build your dbt data models in the production schema in the PC_DBT_DB database as defined in the Snowflake connection.
  • Click on the deploy tab and select Jobs. You will see the preconfigured Partner Connect Trial Job. Click on the job and Settings to modify it. 
  • Click on Edit to make changes and then change the name of the job to Production job. Click on Save after making changes.
  • Now, you will run the job. Navigate to Deploy > Jobs. Click on the job name, and it will take you to the job run history page, where you will click on the Run run button to run the job. 
dbt Data Modeling: image 8
Image Source
  • Go to Snowflake to ensure that everything is built in the production schema. Check the database objects in the Snowflake account, and you can see the production schema in the PC_DBT_DB database. Click on the schema to see the models you have deployed.
dbt Data Modeling: image 9
Image Source

Scaling dbt Models

The modularity of dbt workflows enables you to scale your dbt projects. However, you must embrace a better strategy for your dbt projects to scale without causing operational issues. Here are some of the best methods you should follow:

Enhance Documentation 

dbt has a superior process for documentation to obtain context and collaborate better. However, documentation of dbt can be further improved by adding business logic. Usually, developers only include the tech concepts. But, adding the business logic allows for better understanding. You can include why a particular column was transformed and how it caters to the business requirements. It can be further enhanced by providing more context to the documents as you modify a transformation at different intervals. This enables other developers to know how the transformation changed over time based on the varying requirements. Such in-depth information simplifies the workflow while scaling your dbt projects. 

Set Data Pipeline Rules

It is crucial to precisely define the data pipeline rules to scale dbt models. Ideally, pipeline owners should set the rules as they work closely with the business development team. 

For instance, the transformation logic varies for the same dataset based on the business requirements. It is primarily because different end-users want the same data for varying use cases. Therefore, allowing the pipeline owner to define the rules is essential. This eliminates the discrepancies to help you build quickly at scale. 

Data Modeling Best Practices 

There are mainly four best practices for your data model to enhance its performance.

  • Understand the Business Requirements

Data modeling processes mainly focus on understanding your data better in an efficient way. In businesses, data modeling professionals capture business requirements to understand which data to prioritize, collect and make accessible to the users. After knowing the requirements, data professionals can get a complete idea about what the stakeholders or users want to convey from the data. 

  • Start with Primary Model

Start with the primary data model that sets the foundation for all other models. But ensure that your primary model is reliable before moving ahead. As you grow, you can introduce more layers on top of your primary data model. This practice will allow you to keep incorporating new business requirements in the data models as you scale.  

  • Naming

In the data modeling processes, naming the datasets is essential for a streamlined workflow. One of the ideal practices which professionals follow while naming is to pick a naming scheme. For example, you can use marketing schemas to hold the tables most relevant to the marketing team. You can also use analytics schemas to store advanced marketing metrics like long-term value (LTV).

  • Materialization

Materialization is one of the critical concepts in data modeling processes. With materialization, you can determine whether the relation is created as a table or a view. You can precompute any required calculations if the relationship is created as a table. This means that your user will receive a faster query response. Whereas if you create the relationship as a view, users will get more up-to-date data when they query. However, the response time will be slower.

Common dbt Data Modeling Mistakes and their Solutions

You need to analyze how your dbt data models will look like before even writing a single line of code. While designing data models, professionals always make common mistakes. Some of the mistakes, along with their solutions, are listed below:

  • Not Having a Specific Business Reason for Designing a Data Model 

When engineers design data models, they have many reasons to build them. But they often need to remember the primary business need for a particular data model. While designing data models, engineers should consider factors like readability, modularity, and performance in mind. They should analyze how businesses can use the data model and how often it will be used.

As a data engineer or a data analyst, it is essential to observe what reports and dashboards are helpful for businesses. They should know what kind of data is pulled or received from the data model and how often it is updated. 

  • Building Data Models that are Difficult to Debug

While building data models, engineers mostly follow a modular approach. This is because modular data models are already broken down into smaller ones that can be debugged easily. Therefore, engineers can quickly discover the mistake when something goes wrong in the code rather than rewriting the entire code.

With the dbt tool, debugging smaller pieces of code becomes more accessible than the vast data model. It also supports testing at the source within your data models, improving the debugging process and data quality.

Future of dbt Data Modeling

Due to its extensive features, dbt enhances the speed of data modeling processes. It benefits early-stage companies by expediting the development time for product launches. In a modular approach, every consumer or producer of data models within an organization can start with foundational data modeling work. They do not have to start with the source data every time. With modular dbt data models, you can reference foundational data models in multiple places instead of starting from scratch.

Besides referencing, modular dbt models can also enhance debugging and testing speed. Due to the modular approach, dbt users can code data models in smaller pieces that can be easily debugged and tested.


This article discusses the dbt data modeling tool, techniques, patterns, and practices. dbt is an effective tool for organizations to implement data models. With dbt, data engineers and analysts can allow organizations to follow best practices like version control, documenting, and testing data models and transformations. Organizations can use the open-source version of the dbt tool to keep the license costs low or use dbt Cloud for simplicity.

For cases when you rarely need to replicate data, your engineering team can easily do it. Though, for frequent and massive volumes of data transfers from multiple sources, your engineering team would need to constantly monitor and fix any data leaks. Or you can simply hop onto a smooth ride with cloud-based ELT solutions like Hevo Data which automates the data integration process for you and runs your dbt projects to transform data present in your data warehouse. At this time, the dbt Core™ on Hevo is in BETA. Please reach out to Hevo Support or your account executive to enable it for your team.

Visit our Website to Explore Hevo

Offering 150+ plug-and-play integrations and saving countless hours of manual data cleaning & standardizing, Hevo Data also offers in-built pre-load data transformations that get it done in minutes via a simple drag-n-drop interface or your custom python scripts. 

Want to take Hevo for a spin? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

Share your experience of learning about Data Build Tool(dbt)! Let us know in the comments section below!

No-Code Data Pipeline for Your Data Warehouse