Data Transformation

Unlocking the Power of Data Transformation with dbt (Part II)

In the second part of our series we discuss how dbt enables modular data modeling, dbt products, and the differentiating features of dbt.
Arti Gupta
5 min to read

In our previous blog on dbt, we gave you a brief overview of dbt and the move from ETL to ELT. In the second part of our series we will discuss data modeling, how dbt enables modular data modeling, dbt products, and the differentiating features of dbt. 

What is Data Modeling?

Data modeling involves creating a visual depiction of an information system by establishing connections between data elements. An example of data modeling is structuring data into tables, consisting of rows and columns that facilitate relationships between data points. In business, data modeling is crucial for obtaining high-quality, consistent, and well-structured data to support various applications. When creating data models, it is important to embrace modularity and define relationships among the collected data.

How does dbt enable Modular Data Modeling?

Data modeling using dbt is a strong technique that enables modular data transformation, which is especially useful for business intelligence. It entails developing dbt models using SQL SELECT statements that define transformation logic. These models act as reusable building blocks, allowing for data modeling versatility. Before beginning data modeling with dbt (whether dbt Cloud or dbt Core), consider issues such as data source location, the data model's incremental or full load nature, and the intended type of data modeling. In addition, for version control, you also need to establish a git repository.

There are two types of files in a dbt project: configuration files and SQL files. Config files are used to indicate sources, paths, versions, and how models should be built in the target environment. The dbt_project.yaml file normally contains these configuration settings. SQL files, on the other hand, are where data models are defined. These files are frequently made up of common table expressions, followed by a final select statement that generates the desired output. 

Here are some other key aspects of dbt that facilitate modular data modeling:

  • Model Dependencies: dbt allows you to define model dependencies and connect distinct models in your data modeling project. By specifying these dependencies, you can construct a modular framework in which changes to one model automatically trigger updates to dependent downstream models. This dependency management function speeds up the data transformation process by ensuring that changes are implemented in the correct order, hence preserving data integrity and consistency. 
  • Macro Functions: Furthermore, dbt allows you to combine SQL with Jinja, a sophisticated templating language. Your dbt project is transformed into a SQL programming environment by employing Jinja, providing features beyond typical SQL. Jinja macros, like functions in other programming languages, are reusable code snippets. These macros can be defined in .sql files, which are normally stored in your project's macros directory. The use of macros reduces code repetition across various models, improving efficiency and code maintainability.
dbt_data build tool_DataChannel
  • Incremental Builds: dbt can also support incremental builds, which optimizes the data modeling process by selecting and rebuilding only the affected models. This effective method reduces processing time and resource use. Unnecessary computations for unchanging parts of the data model are avoided, resulting in better speed and less load on data infrastructure. Incremental builds improve modularity by allowing targeted updates without the need to reprocess unaltered data model components.

dbt Products

dbt is made up of two main components: dbt Core and dbt Cloud. dbt Core is an open-source solution that enables data teams to convert data using best practices in analytics engineering. It is a command-line tool for developing dbt projects and running commands to build tables or views in the data warehouse. dbt Core is free to use and open-source software licensed under the Apache Licence.

dbt Cloud, on the other hand, is a comprehensive platform based around dbt Core. It has a user-friendly web-based interface that makes it easier for teams to collaborate on data modeling tasks. dbt Cloud provides a hosted environment, which speeds up the setup procedure. It has sophisticated capabilities such as metadata management, an in-app task scheduler, observability tools, and an integrated development environment (IDE)

Differentiating Features of dbt

dbt (Data Build Tool) provides several benefits when it comes to testing, deploying, documenting, and version controlling data transformations. *


In dbt, tests are written as SQL queries that are designed to find "failing" entries that contradict your claims. For example, if you declare that a column in a model should be unique, the test query will look for duplicates. If the test query returns no failing rows, it indicates a successful result, proving the validity of your assumption. dbt defines tests in two ways: singular tests and generic tests. Singular tests require SQL queries to be written in.sql files within the test directory, whereas generic tests work similar to macros. Because generic tests are flexible, they may be referenced and applied to different components within your dbt project, increasing code reusability and making maintenance easier.


Through its organized framework and support for incremental builds, dbt simplifies the deployment of data models. This means that model updates can be deployed without having to rebuild the entire dataset, greatly simplifying the deployment process. Deploying dbt in a production environment requires Data Engineers or Analysts to set up scheduled dbt jobs rather than manually executing dbt commands. These production dbt jobs are in charge of creating the necessary tables or views on which business intelligence tools and end users rely. On the other hand, with dbt Core, data orchestration tools like Airflow or other advanced ELT (Extract, Load, Transform) tools can be utilized to achieve the same deployment functionality.


dbt includes the useful function of automatic documentation production, which collects information from data model’s code and metadata to provide thorough and up-to-date documentation for data transformations. The generated documentation includes critical elements like the project's model code, any associated column tests, and other pertinent information. It also contains important data warehouse information, such as column data types and table sizes. Furthermore, dbt supports the introduction of descriptions for models, columns, sources, and other components, which improves the clarity and completeness of the documentation.

dbt_data build tool_DataChannel

Version Controlling:

dbt encourages data team collaboration by employing version control systems (VCS) such as Github. Each team member can work on their own branch of the project, making independent changes. They can design new models, edit existing ones, add tests, and make any other changes that are required. These modifications are recorded in the VCS, preserving the project's whole history.

Team members can evaluate each other's code, provide input, and assure the quality and correctness of the changes by using VCS tools such as pull requests or merge requests. This collaborative approach supports higher code quality, information sharing, and a continuous development culture within the data team.

The dbt Slack Community

The dbt Slack community has grown rapidly and has emerged as the most active and engaged group in the modern data stack. With over 25,000 experts globally as members, it provides a broad and diverse network of persons skilled in dbt. This rise in growth shows the dbt approach's growing prominence and widespread adoption among the data community. The dbt Slack community continues to thrive by prioritizing collaboration, knowledge sharing, and adapting to the needs of its growing size, harnessing the aggregate expertise of its members to foster innovation and excellence in data modeling.

Stay tuned for our next blog in the series on unlocking the power of data transformation with dbt in which we will discuss about dbt projects, and implementing dbt for downstream business use cases. Meanwhile, if you want to learn more about how DataChannel enables data transformations, book a quick demo call with us.

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