What is dbt in Data Engineering?

TL;DR — dbt (data build tool) is an open-source analytics engineering framework that applies software engineering best practices to data transformation. It enables data teams to build, test, and document data models in their data warehouse using SQL and Python, ensuring data quality and maintainability.

Data engineers often grapple with complex, unmanageable SQL scripts and a lack of best practices in their data transformation layer. This can lead to inconsistent data, debugging nightmares, and slow development cycles. The challenge is building reliable, scalable data models that are easy to maintain, test, and understand.

This article clarifies what dbt (data build tool) is in data engineering, how it addresses these pain points, and its central role in the modern data stack. You’ll learn its core functionalities, differentiate it from other tools, and discover how Kestra provides the orchestration layer to run dbt workflows with enterprise-grade reliability and visibility.

How dbt transforms data in data engineering

dbt has become the industry standard for the “T” (transform) in ELT (Extract, Load, Transform) pipelines. Instead of transforming data before it enters the warehouse, dbt works directly on raw data that has already been loaded, leveraging the power of modern cloud data warehouses like Snowflake, BigQuery, and Databricks.

Defining dbt: a software engineering approach to data

At its core, dbt is a development framework that brings software engineering principles to analytics and data modeling. It allows data teams to write modular, version-controlled, and testable SQL. By treating analytics code like application code, dbt enables teams to build more reliable and maintainable data pipelines.

Transformations in dbt are written as SELECT statements, and dbt handles the boilerplate Data Definition Language (DDL) to materialize these queries as tables or views in the target database. It uses Jinja, a popular templating language, to inject logic like if statements, loops, and macros into SQL, making the code more dynamic and reusable. This approach helps manage dependencies between models, ensuring that transformations run in the correct order.

Core functionalities: modularity, testing, and documentation

dbt’s power comes from a set of core features that address common pain points in data transformation:

• Modularity: You can break down complex transformations into smaller, reusable SQL models that reference each other. This creates a clear dependency graph (DAG) of your data models, making them easier to understand and debug.
• Version Control: dbt projects are typically managed in Git repositories. This enables collaboration, code reviews, and a full history of changes, aligning data work with standard DevOps practices.
• Testing: dbt allows you to write tests to assert the quality of your data. You can define generic tests (like checking for nulls or uniqueness) and singular tests for custom business logic. These tests run as part of your dbt workflow, catching data quality issues before they impact downstream dashboards and analyses.
• Documentation: dbt automatically generates documentation for your project, including model descriptions, column definitions, and a visual representation of the dependency graph. This living documentation makes it easier for new team members to get up to speed and for stakeholders to understand the data lineage.

There are two main ways to use dbt: dbt Core vs. dbt Cloud. dbt Core is the open-source command-line tool, while dbt Cloud offers a managed service with a web-based UI, scheduling, and other enterprise features.

Why data transformation needs dedicated orchestration

While dbt is excellent at managing transformations within the data warehouse, it’s not a complete data orchestration platform. dbt Cloud provides a basic scheduler, but production data pipelines often require more sophisticated capabilities that go beyond what dbt offers natively.

Here’s why a dedicated orchestrator is essential for running dbt at scale:

• Dependency Management: Real-world pipelines involve more than just dbt. An orchestrator can manage dependencies between dbt jobs and other tools in your stack, such as data ingestion tools (like Airbyte or Fivetran), reverse ETL tools, and BI platform refreshes.
• Advanced Scheduling and Triggers: Orchestrators provide flexible scheduling options (e.g., event-driven triggers, complex cron expressions) that matter for timely data delivery. You can trigger a dbt run based on the successful completion of an upstream data loading task, a file landing in S3, or an API call.
• Error Handling and Alerting: A capable orchestration tool offers configurable retry policies, timeouts, and alerting mechanisms. If a dbt model fails, the orchestrator can automatically retry the job or notify the on-call team via Slack, PagerDuty, or email.
• Observability and Lineage: While dbt provides project-level documentation, an orchestrator gives you a centralized view of all your data pipelines, not just the transformation layer. This end-to-end data observability is critical for monitoring performance and troubleshooting failures across your entire data stack.
• CI/CD for Data Pipelines: Integrating dbt into a larger CI/CD workflow is a common pattern. An orchestrator can automate the process of testing and deploying dbt changes, ensuring that only quality code makes it to production. You can learn more about these practices in our guide to CI/CD for data pipelines.

Orchestrate dbt with Kestra: a production-ready example

Kestra allows you to define, schedule, and monitor your entire data workflow, including dbt transformations, as code. The following example shows a Kestra flow that clones a dbt project from a Git repository, runs the transformations, and sends a notification to Slack.

id: dbt-git-and-slack
namespace: company.team.production

tasks:
  - id: clone-repo
    type: io.kestra.plugin.git.Clone
    url: https://github.com/your-org/dbt-project.git
    branch: main

  - id: dbt-setup
    type: io.kestra.plugin.scripts.shell.Commands
    runner: DOCKER
    docker:
      image: python:3.11-slim
    commands:
      - pip install dbt-bigquery
      - cp profiles/dbt/profiles.yml .

  - id: dbt-run
    type: io.kestra.plugin.dbt.cli.DbtCLI
    runner: DOCKER
    docker:
      image: python:3.11-slim
    commands:
      - dbt deps
      - dbt build

errors:
  - id: alert-on-failure
    type: io.kestra.plugin.notifications.slack.SlackExecution
    url: "{{ secret('SLACK_WEBHOOK_URL') }}"
    channel: "#data-alerts"

A few things are worth noticing in this flow:

• Declarative & Version-Controlled: The entire workflow, including the dbt execution and the surrounding tasks, is defined in a single YAML file. This can be version-controlled in Git alongside your dbt project.
• Environment Isolation: Each task runs in its own isolated environment using Docker. This ensures that dependencies for different tasks don’t conflict.
• Secrets Management: The Slack webhook URL is securely managed using Kestra’s secret management system, preventing sensitive information from being hardcoded in your workflow definition.
• Automatic Alerting: The errors block automatically triggers a Slack notification if any task in the flow fails, providing immediate visibility into pipeline issues.

When Kestra enhances your dbt workflows

Kestra is a powerful orchestration layer for dbt because it is language-agnostic and built for modern data and platform engineering teams. For data engineers, this means you can coordinate dbt runs with tasks written in Python, Shell, R, or any other language, all within the same workflow.

Kestra’s event-driven architecture allows you to trigger dbt jobs based on real-time events, such as a new file arriving in a cloud storage bucket or a message in a Kafka topic. This enables more responsive and efficient data pipelines compared to simple time-based schedules. When comparing dbt on Airflow vs. Kestra, Kestra’s declarative YAML approach and simpler architecture often result in lower operational overhead and faster development cycles.

Where dbt pays off for data teams

Adopting dbt brings significant benefits, transforming how data teams operate and deliver value. As noted in the 2026 Data Engineering Trends, the focus is shifting towards building reliable, product-grade data assets, and dbt is a key enabler of this shift.

• Improved Data Quality and Reliability: With integrated testing, data teams can catch issues early, ensuring that the data delivered to stakeholders is accurate and trustworthy.
• Increased Productivity: Reusable models, macros, and automated documentation significantly speed up the development process. Analytics engineers can build and iterate on data models much faster.
• Enhanced Collaboration: By using Git for version control and promoting code reviews, dbt fosters a collaborative environment where team members can work together on a shared codebase. As seen in the Gorgias case study, applying Infrastructure as Code best practices to data engineering with dbt and Kestra leads to more reliable and scalable systems.
• Better Governance and Lineage: The auto-generated documentation and clear dependency graph provide transparency into how data is transformed, making it easier to govern data assets and comply with regulations. For example, Riverside’s analytics team used Kestra to connect dbt Cloud, Snowflake, and Hightouch into a single dependency-aware pipeline, gaining full control and visibility.

Related concepts in data engineering

dbt is part of a broader set of tools and concepts in modern data engineering. Understanding these related areas will provide a more complete picture of where dbt fits.

• dbt vs. SQLMesh: Explore how SQLMesh compares to dbt as another modern data transformation tool.
• ETL Pipeline Tools: Discover other tools that handle the Extract and Load parts of the data pipeline, which feed data into the transformation layer where dbt operates.
• Fivetran and dbt Merger: Understand the implications of the merger between a leading data ingestion tool and dbt Labs for the future of ELT pipelines.
• Mage.ai Alternatives: See how dbt fits into the landscape of data pipeline tools when compared with other integrated platforms.

By integrating dbt with a powerful orchestration platform like Kestra, you can build, run, and monitor end-to-end data pipelines that are reliable, scalable, and maintainable. To see more examples, check out our blueprints for running dbt transformations on Google BigQuery or building an ETL pipeline with Airbyte and dbt.