What is DBT in Data Engineering? A Comprehensive Guide

Blog > What is DBT in Data Engineering? A Comprehensive Guide

Data transformation is a crucial step in ensuring the quality and usability of data. With the ever-growing volume and complexity of data, data engineers and analysts need a reliable and efficient way to transform data into actionable insights. This is where DBT (Data Build Tool) comes in – a powerful open-source tool that simplifies data transformation and analysis. In this comprehensive guide, we will dive into the world of DBT, exploring its features, benefits, and practical applications in data engineering. Whether you’re a seasoned data professional or just starting out, this guide will help you explore the full potential of DBT and take your data transformation skills to the next level.

What is DBT?

DBT is the open Source tool, which provides the framework, using which Data Engineers/Data Scientists/Data Analysts, can build various Models or Data Pipelines using SQL query. These models can be built with the necessary business logic or transformation logic and can be tested, scheduled and deployed into the Datawarehouse system.

DBT tool can be used effectively in the Transformation layer of any ELT system in Data warehouse world. This can provide Data Analysts and Scientists to build the data pipelines, helps to build robust data management system and achieve their data analytical needs simply by using the SQL language.

Illustration explaining Data Build Tool (DBT) and its core principles

DBT comes in 2 different Flavors:

1) DBT Core: This is command line based tool which can be used to transform the data

2) DBT Cloud: This is UI based tool which can be used to transform the data

DBT tool can work compatible with All major Cloud provides like AWS, GCP, Azure etc.

What is DBT in Data Engineering?

DBT is a crucial tool in data engineering, as it enables data engineers to transform data in their warehouses by writing SQL statements. It does not extract or load data but is designed to be performant at transforming data already inside a warehouse. DBT empowers data engineers to work more like software engineers, following best practices in software engineering to ensure maintainable and scalable data infrastructure.

What is a Data Model in DBT?

Illustration of the DBT Layer architecture showcasing its components and data flow

In DBT, a data model is a SQL file that defines data transformations. These models can depend on other models, have tests defined on them, and can be created as tables or views. The names of models created by DBT are their file names, and DBT uses file names and references in other files to automatically define the data flow.

Why Do We Use DBT?

DBT is used to transform data in data warehouses, making it easier to work with data. It provides a consistent and standardized approach to data transformation and analysis, ensuring data consistency and reproducibility. DBT also supports collaboration and documentation through YAML files and human-readable comments.

Core Functionalities of DBT for Seamless Data Workflows

  1. Data Transformation: DBT transforms data in data warehouses by writing SQL statements.
  2. Version Control: DBT supports version control, allowing data engineers to track changes and revert to previous versions of their models.
  3. Collaboration: DBT supports collaboration by allowing multiple users to work on the same project with defined roles and permissions.
  4. Testable: DBT allows data engineers to write tests for their models to ensure they work as expected and identify potential issues before deploying them to production.
  5. Repeatable: DBT ensures repeatable data transformations across different data sources and environments.

Implementation Strategy

It is crucial to establish an effective implementation strategy, this strategy serves as a roadmap, guiding the execution of each step in the process. DBT tool, by itself comes with Vertical Control facility, which can help the Modelers to roll back to previous version of the code. This facility can help to do Continuous Development and Continuous Deployment, thus helping to achieve the analytical process quicker, highly effective and lesser riskier

By outlining clear objectives, timelines, and resource allocation, the implementation strategy ensures smooth execution and maximizes the success of the project. Key considerations include identifying stakeholders, defining roles and responsibilities, setting milestones, and allocating necessary resources. With a well-defined implementation strategy in place, the team can proceed with confidence, knowing they have a structured plan to follow.

A. Finalize Business Logic

  1. Description: This step involves understanding and defining the specific rules and requirements that govern how data should be processed and transformed to meet business objectives.
  2. Action: Gather input from stakeholders to clarify business needs and logic requirements.
  3. Outcome: Clearly defined business logic serves as the foundation for building accurate data pipelines and models.

B. Build DBT Models

  1. Description: Utilize DBT (data build tool) to construct data transformation pipelines and models within a development environment.
  2. Action: Write SQL queries within DBT to transform raw data into structured formats suitable for analysis.
  3. Outcome: DBT models provide a structured and efficient framework for data transformation, enabling easier maintenance and scalability.

C. Schedule and Run Models

  1. Description: Establish scheduling mechanisms to automatically execute DBT models at specified intervals, ensuring timely data updates.
  2. Action: Configure scheduling tools or platforms to trigger the execution of DBT models based on predefined schedules.
  3. Outcome: Regular execution of models ensures that transformed data is up-to-date and available for analysis as needed.

D. Test Transformed Data

  1. Description: Validate the accuracy and integrity of transformed data generated by DBT models through systematic testing procedures.
  2. Action: Develop test cases to verify that transformed data meets expected outcomes and adheres to defined business logic.
  3. Outcome: Thorough testing mitigates the risk of errors or inconsistencies in the transformed data, enhancing data quality and reliability.

E. Deploy to Higher Environment

  1. Description: Promote data pipelines or DBT models from the development environment to higher environments such as staging or production.
  2. Action: Utilize deployment tools or processes to transfer DBT models and associated configurations to target environments.
  3. Outcome: Deployed pipelines or models make transformed data accessible for broader usage, facilitating decision-making and analysis across the organization.


  1. Description: Create comprehensive documentation that outlines the process, methodologies, and technical details of building and maintaining data pipelines and DBT models.
  2. Action: Document key steps, configurations, and dependencies involved in each stage of the data transformation process.
  3. Outcome: Well-documented processes and models serve as valuable resources for knowledge sharing, troubleshooting, and future enhancements

Practical Use Cases for DBT

let’s explore some practical scenarios where DBT can be effectively utilized. From streamlining data pipelines to ensuring data quality, DBT offers solutions for various data engineering and analytics challenges. Whether you’re working with structured or semi-structured data, DBT’s flexibility and scalability make it suitable for a wide range of use cases. Examples include building analytical data models for business intelligence, automating data transformations for reporting, and orchestrating complex data workflows for machine learning applications. By understanding these use cases, teams can Utilize the potential of DBT to drive actionable insights and informed decision-making across the organization.

A. Building and Maintaining Data Infrastructure

  1. Description: DBT simplifies the process of constructing and managing data infrastructure that can adapt and grow with your organization’s needs.
  2. Explanation: With DBT, you can easily define data models and transformations in SQL, allowing for the creation of scalable pipelines that efficiently process and organize data.
  3. Benefit: By leveraging DBT’s capabilities, teams can build robust data infrastructure that is easier to maintain and scale over time, reducing the complexity and effort required for ongoing management.

B. Data Transformation

  1. Description: DBT offers a streamlined and standardized method for transforming raw data into structured formats suitable for analysis.
  2. Explanation: Through its SQL-based approach, DBT provides a consistent framework for defining and executing data transformations, regardless of the underlying data sources.
  3. Benefit: By using DBT for data transformation, organizations can ensure that data is processed accurately and efficiently, enabling faster insights and decision-making based on reliable information.

C. Version Control and Testing

  1. Description: DBT includes features for version control and testing, allowing teams to track changes to data models and ensure their reliability.
  2. Explanation: With DBT, you can manage changes to data models using version control systems like Git, enabling easy collaboration and tracking of modifications over time.
  3. Benefit: By implementing version control and testing with DBT, teams can maintain data consistency and reproducibility, reducing the risk of errors and discrepancies in analysis results.

D. Collaboration and Documentation

  1. Description: DBT facilitates collaboration among team members and promotes documentation through the use of YAML files and human-readable comments.
  2. Explanation: Through YAML configuration files, teams can define data models and transformations in a clear and structured format, making it easier to collaborate and understand each other’s work.
  3. Benefit: By encouraging collaboration and documentation, DBT helps teams share knowledge, onboard new members, and maintain a comprehensive record of data transformation processes, enhancing transparency and efficiency across the organization.

Pros and Cons of DBT

It is essential to understand the strengths and limitations of DBT. As a powerful data transformation tool, DBT offers numerous benefits such as open-source availability, built-in version control, and streamlined testing capabilities. However, it also comes with certain drawbacks, such as the absence of a dedicated debugging facility. By weighing these factors, organizations can make informed decisions about adopting DBT for their data engineering and analytics needs.


1. Open-source and available in various free and paid plans:

Description: DBT is open-source, meaning its source code is freely available for modification and redistribution. Additionally, it offers a range of plans, including both free and paid options, catering to different organizational needs and budgets.

Benefit: The open-source nature of DBT promotes community collaboration and innovation, while the availability of different plans ensures scalability and flexibility for organizations to choose the right fit for their requirements.

2. In-built version control:

Description: DBT comes with built-in version control features, enabling users to manage changes to data models and transformations effectively. It integrates seamlessly with version control systems like Git, allowing teams to track modifications, revert changes if necessary, and collaborate efficiently.

Benefit: Version control enhances transparency and accountability in data pipeline development, facilitating collaboration among team members and ensuring data integrity throughout the development lifecycle.

3. In-built testing:

Description: DBT provides native support for automated testing, enabling users to validate the accuracy and consistency of data transformations. By defining test cases within DBT, users can automatically verify that their data pipelines produce expected results and meet predefined quality standards.

Benefit: Automated testing helps identify and mitigate errors early in the development process, reducing the risk of data inaccuracies and ensuring the reliability of analytical insights derived from the data.

4. No separate skills or expertise required to use and build models:

Description: DBT simplifies the process of building data models by leveraging SQL, a widely-used and familiar language for data manipulation and analysis. Users with SQL proficiency can easily create and manage data transformations within DBT without the need for specialized skills or expertise.

Benefit: By leveraging existing SQL knowledge, teams can quickly adopt DBT and accelerate the development of data pipelines and models, streamlining the path to actionable insights and data-driven decision-making.


1. No debugging facility:

Description: One limitation of DBT is the absence of dedicated debugging tools or features for troubleshooting issues within data models. Without built-in debugging capabilities, users may face challenges in identifying and resolving errors or inconsistencies in their data pipelines.

Drawback: The lack of a debugging facility can potentially prolong the troubleshooting process and increase the complexity of resolving issues, requiring users to rely on manual inspection and testing methods to diagnose and rectify problems in their DBT projects.

Common DBT Handling Challenges for Data Engineers

Data engineers often encounter several challenges when working with DBT (Data Build Tool). These challenges can impact the efficiency and effectiveness of their data transformation processes. Understanding and addressing these issues is crucial for optimizing workflows and ensuring smooth data operations. In the following sections, we will explore some of the most common DBT handling challenges and provide practical solutions to overcome them.

1. Data Quality Issues

Ensuring data quality and consistency across different data sources is critical for making accurate business decisions. Data quality issues can arise from various factors, including incomplete data, duplicate records, and inconsistent data formats. To address these issues, it’s essential to implement data validation rules, regular data audits, and automated data cleansing processes. By doing so, businesses can ensure that their data is accurate, complete, and consistent, leading to more reliable insights and better decision-making.

2. Data Integration

Integrating data from multiple sources into a single data warehouse can be a complex process. It involves combining data from different databases, applications, and other data repositories into a centralized location. This integration process allows businesses to have a unified view of their data, making it easier to analyze and derive insights. Effective data integration requires the use of robust ETL (Extract, Transform, Load) tools and technologies to efficiently transfer and consolidate data from various sources. Ensuring data compatibility and addressing integration challenges are key to successful data integration.

3. Data Transformation

Transforming data to meet specific business requirements involves modifying and restructuring data to make it usable and relevant for analysis. This process includes cleaning, aggregating, and converting data into the desired format. Data transformation ensures that the data is standardized and aligns with the business needs. This step is crucial for accurate reporting, analytics, and decision-making. By applying transformation rules and leveraging powerful data transformation tools, businesses can tailor their data to support their unique objectives and achieve meaningful insights.

How Can DataFinz and DBT Integration Simplify Data Transformations?

Integrating DataFinz with DBT simplifies data transformations by creating a seamless workflow for data engineers. This integration ensures efficient, standardized, and reproducible data handling, improving data consistency and quality. DataFinz manages data ingestion, while DBT handles transformations with reusable SQL queries. This synergy enhances collaboration, scales with business growth, and automates workflows, allowing engineers to focus on analysis rather than manual tasks. Ultimately, this integration leads to more effective data management and utilization, resulting in faster insights and better decision-making.