In any data-driven organization, transforming data is challenging due to the sheer volume and complexity of big data. Organizations keep collecting data but find using raw data to obtain insights difficult. This leads to data silos, where data resides in data stores but is not used for long. The primary reason for data silos is the lack of ability to transform the data as and when it arrives. To address these issues, organizations leverage serval tools. One of those tools is dbt, which allows you to transform data quickly. 

If you want to use this tool optimally, you must follow dbt best practices. No worries! This article brings you the hand-selected top dbt best practices you can apply while using dbt.

What is dbt?

dbt best practices - dbt logo
Image Source

Dbt is a data transformation tool that allows organizations to version control, test, and deploy transformation projects. The transformation in dbt is carried out within a data warehouse, making it straightforward for data engineers to manage data effectively. It supports several types of transformations—view, table, incremental, and ephemeral—to provide the necessary flexibility while handling raw data. You can transform data using simple SQL SELECT statements or Python programming for implementing complex transformations. 

Dbt can be embraced either through dbt core or dbt cloud. Dbt core is an open-source command line interface. And dbt cloud is a managed service for UI-based workflows.

Top 14 dbt Best Practices 

Dbt best practices are essential to obtain the desired output—better transformation, enhanced collaboration, and performance boost. Here are the top dbt best practices: 

  1. Avoid ETL

The core principle of dbt is to transform after the data has been stored in the data warehouse. It is contrary to what is practiced widely across different data-driven organizations. Often data is transformed before loading it into a data warehouse. As a result, data engineers keep switching between different applications for transformation and storage. To avoid the clutters while handling data, dbt empowers you to transform data within data warehouses. The idea behind dbt is to use ELT, not ETL, in data warehousing.

  1. Create a dbt Style Guide

As raw data comes from various sources, it may have different names for the same column. This leads to duplicate data and issues while referencing the data during transformation. Raw data also have different data types, making it challenging to modularise the code for an efficient transformation process. To avoid such issues, you should have dbt style guide. You could set dbt best practices for column naming (snake case) conventions and data types to standardize for consistency.

You can have a style guide for how to structure and name Common Table Expressions. The style guide further extends to how you should write SQL commands. For example, SQL should end with a simple select statement, use a trailing command, and more.

  1. Structure dbt Projects

Project structures are essential to enhance collaboration, as users do not want to spend time identifying the correct folder every time they start a task. As one of the dbt best practices, standardizing the directory structure based on the business requirements can simplify the entire workflow. 

As a result, users can spend more time building solutions than finding folders. Generally, when you start the project with the default setting, you get all the necessary dictionaries. But, the difference occurs in the models directory when you build according to your use case requirements. Generally, models can have three layers: staging, intermediate, and marts.

Staging is where you build the foundation of your projects. In staging, you can bring the data and apply transformations to create Common Table Expressions. After modularizing the code, you move to the intermediate step. Now, you start bringing various modular codes together for complex transformations. And in the marts, you have the full-fledged models grouped based on business use cases like finance and marketing.

  1. Use Source to Reference Data

Dbt Sources are an efficient way of referencing raw data during transformation. You create dbt models, which are SQL SELECT statements, for transformation. These models reference raw data and transform it according to the business requirements. 

However, directly referencing raw data can become clumsy and cause numerous problems while building dependencies. A significant problem associated with direct referencing is when you are changing the name of the column. 

In such cases, you will have to update the name everywhere the associated data is referenced. You can use dbt sources to declare tables in the .yml file located in the models/ directory. If you ever need to change the name or the associated raw data source, you would only be required to update the dbt sources file.

  1. Model Building dbt Best Practices

As the transformation codes are built as models, it is important to implement dbt best practices in models. You should start with configuring models to include materialization types. There is more than one way to change the materialization types. 

However, updating the materialization in the model block is recommended. Further, you should organize models in different schemas because having multiple models in the target schema by default can be difficult to identify based on use cases. You can create models based on departments like marketing, finance, and support. 

Having models in specific directories would allow teams to focus on the models that they are responsible for. You can also enhance working with models by adding tags. Tags can be used based on how frequently you run the models. Organizing models are critical to the success of dbt projects as models are the core of the transformation. 

  1. Choose the Right Materialization

Dbt offers four types of materialization—table, view, incremental, and ephemeral. By default, materialization is set to view. These are similar to SQL views, a type of virtual table that doesn’t take up your storage space. Views just define the logic that is used to fetch data. 

As a result, it doesn’t change the underlying data. However, views are slow and more costly to query than tables. Materializing with tables is mainly used while transforming data for BI analysis. 

But, unlike views, tables are not updated until you refresh the queries. Both views and tables have their both advantages and disadvantages and can be used according to business requirements. 

You can also use incremental materialization to transform event-style data at regular intervals. Dbt incrementals are widely popular among data engineers as it enables you to transform data with less computation. Finally, the ephemeral models are used for lightweight transformations as it is not directly built into the database. 

  1. Test Your Code

Dbt comes with built-in test cases for non-null, relationships, unique, and accepted values. 

These codes allow you to maintain consistency and enhance the quality of data. You should always write test cases for your code to ensure the dbt models work without errors. Test cases also help you debug the code when you encounter new errors. However, generic test cases cannot catch different types of errors your projects can witness. You should create your own generic test cases by creating a test block called <test_name>.

  1. Automate Deployment Tasks

As data handling processes include a lot of different tasks, you would like to automate specific jobs. Since dbt allows you to create reusable or modularized code, it is ideal for automating deployment tasks. However, dbt core doesn’t support automation through scheduling. You can use dbt cloud to schedule the transformation jobs based on the requirements. 

  1. Limit Your Data When Possible

While working with big data, you might often encounter transformations that could take hours. Massive volumes of data can slacken your workflows as you would have to wait for the processed data from the next step. If not in production, you can, at least, limit the data size in the testing by using filters. 

  1. Use Hooks to Manage Privileges on Models

Hooks in dbt provide more advanced capabilities to run custom SQL commands. There are four types of hooks: pre-hook, post-hook, on-run-start, and on-run-end. To manage privileges, post-hook is used to run grant statements. However, the latest version of dbt recommends using grants resource configuration to automatically apply for the permissions when the dbt model runs.

  1. Break Complex Models up into Smaller Pieces

As the business requirements keep evolving, dbt models can get complex to work with. A dbt model can include several Common Table Expressions (CTE), which are temporary results used in the SQL queries. Working with many CTE within a model can be confusing. To overcome this challenge, you can break CTEs into separate models so that they can be referenced from multiple other downstream applications/models. 

  1. Rename and Recast Fields Once for your dbt Project

Raw data are defined in the dbt source for simplifying referencing while creating models. Since data are referenced in other downstream applications, it is important to standardize the name conventions. Failing to maintain uniformity can result in errors during executions. You need to facilitate a streamlined workflow by renaming and recasting fields of data before making models to transform data. 

  1. Separate Source-Centric and Business-Centric Transformations

Dbt transformation is mainly carried out in two stages: combining data from different sources (source-centric) and transformation for business process requirements (business-centric). With the source-centric transformation, data is joined, re-aligned, and deduplicated. And in business-centric transformations, models are built to provide data to downstream applications. It is recommended to maintain separate models for both kinds of transformations. This will allow business-centric models to reference source-centric models and avoid rewriting the same code. 

  1. Prefer Star Schemas over Snowflake Schemas

Although star schema and Snowflake schema have a lot of similarities, it is recommended to use star schema for simplicity. Snowflake has an additional level of nesting that can help in analytics but can be difficult to query. Due to its structure, Snowflake also takes up more storage space. On the other hand, querying in a star schema is simple and takes less storage space. 


Dbt can help you transform data for your downstream applications. But, you must embrace dbt best practices to ensure a smooth process in data workflows. Failing to incorporate the right dbt best practices can lead to inefficiencies and project failure. Also, you must consider your business objectives while creating your dbt best practices. As you continue to use dbt for your firm effectively, there is still a gap that remains! You need to pull data from all your sources and load them into your data warehouse. 

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 dbt best practices! Let us know in the comments section below!

Manjiri Gaikwad
Freelance Technical Content Writer, Hevo Data

Manjiri loves data science and produces insightful content on AI, ML, and data science. She applies her flair for writing for simplifying the complexities of data integration and analysis for solving problems faced by data professionals businesses in the data industry.

All your customer data in one place.