What is dbt used for in data engineering?
dbt (Data Build Tool) is an essential open-source tool capable of transforming raw data into structured formats. It’s primarily focused on data transformation, transforming raw data into organized, meaningful data for analysis. While traditionally linked to the ELT (Extract, Load, Transform) paradigm – where data is first loaded into a data warehouse and then transformed – dbt doesn't fully replace ELT or traditional ETL (Extract, Transform, Load) methods.
Instead, it significantly enhances the transformation stage by making transformation logic reusable and easier to maintain, allowing teams to repeatedly clean, join, aggregate, normalize, and organize data without rebuilding the broader ingestion pipeline. In this way, dbt increases the efficiency and adaptability of data transformation, positioning itself as an important component of modern data workflows.
Benefits of dbt in data engineering
In data engineering, dbt is used to make transformation logic reusable, version-controlled, testable, and documented, while also making it easier for teams to collaborate on shared data models.
SQL-based data transformation
With dbt, data transformations are written in SQL, a language familiar to data analysts. This simplifies the process of transforming data, making it more accessible to teams that may lack deep technical expertise. dbt models commonly use SQL select statements to build models that transform raw data into business-ready datasets.
For example, teams can define shared logic for revenue, customer segments, or product metrics once and reuse it across multiple reports and dashboards.
Reusable and modular dbt models
One of the primary benefits of DBT is its capability for data modeling, allowing for modular, reusable, and version-controlled SQL scripts that aid in building efficient data pipelines. Data transformation logic can be divided into smaller, manageable pieces, which integrate seamlessly with continuous integration pipelines, making the entire process more efficient.
This reduces duplicated SQL and makes it easier to update one piece of business logic without rewriting every downstream query that depends on it.
dbt and continuous integration and deployment (CI/CD)
dbt projects can be integrated with CI/CD workflows so changes to data models are checked before they reach production. This matters when one model feeds multiple dashboards, reports, or downstream models: a seemingly small SQL change can affect many dependencies.
In a CI workflow, teams can run tests on modified models and related downstream logic before merging a change, helping them catch broken assumptions or failed transformations earlier.
Key components of dbt
Core, fusion, and the dbt platform
The dbt ecosystem now includes dbt Core and Fusion as two distributions of the dbt v2 framework, alongside the managed dbt platform.
dbt Core is the open-source distribution of dbt that teams can install and run in their own environment. It provides the core functionality for building and running data models, testing transformations, managing dependencies, and generating documentation and lineage.
dbt Fusion is an enhanced distribution built on the same underlying dbt v2 engine. It adds capabilities such as deeper SQL understanding, faster project parsing, and column-level lineage. Fusion can also run locally, although some capabilities require a dbt platform account or subscription.
dbt State is a separate capability that can skip unnecessary model rebuilds when the underlying data and transformation logic haven't changed. It can be used with dbt Core as well as the dbt platform.
The dbt platform is the managed environment around these workflows. It adds a browser-based development experience, job scheduling and orchestration, CI, cataloging, governance, and collaboration features for teams that don't want to manage the entire dbt workflow themselves.
Data models and transformations
dbt models are the cornerstone of dbt projects. These models define how raw data is transformed into analytics-ready tables. Each model represents a step in the transformation process, turning source data into a cleaner, more useful dataset for reporting or analysis.
Models can build on one another, which lets teams organize complex transformation logic into clear stages instead of relying on one large query. Most of them use SQL statements to implement the business logic within these models. Python models are also supported on compatible data platforms.
Data documentation and lineage
dbt projects emphasize the importance of data documentation. Teams can document models and columns alongside their transformation code and visualize dependencies between sources and models through data lineage.
This makes it easier to understand where data comes from, how it has been transformed, and which downstream models could be affected by a change.
Integrating dbt with your modern data stack
In a modern data stack, dbt typically handles transformation after data has been ingested into a data platform and before it is consumed by reporting, analytics, or other downstream applications.
dbt is designed to work seamlessly with modern data stacks. Here are a few ways to integrate dbt into your existing data platforms:
- Data warehouses and lakehouses: dbt supports major data warehouses platforms like Snowflake, BigQuery, Redshift, Databricks, and more. You can load data into these platforms and use dbt to transform it.
- Source control and CI/CD: By integrating dbt with version control systems like Git, you ensure that your data transformation code is versioned and collaborative. CI/CD tools can automate the deployment and testing of your dbt models.
- Orchestration tools: dbt works well with orchestration tools like Airflow or Prefect, enabling you to schedule and manage your data workflows effectively. The managed dbt platform also provides orchestration capabilities.
While dbt is responsible for transforming data, it is only one component of a modern data platform, which depends on reliable architecture, source-system integration, orchestration, and scalability. Organizations can use data engineering consulting to address these broader requirements and ensure dbt fits into the wider data environment.
How does dbt support data quality testing?
Data quality problems such as missing values, duplicate records, or broken relationships can make the outputs of otherwise correct transformation logic unreliable.
With dbt, you can automate data quality testing, ensuring that your data models meet the required standards. dbt lets teams set up reusable data checks in YAML and write more specific checks in SQL. It also supports unit tests, which check whether a model produces the expected result from a known input.
Writing tests
dbt allows you to write tests that validate the data produced by your models. These tests can check for things like null values, unique constraints, accepted values, relationships between datasets, and custom business rules
Automated testing
Once tests are defined, teams can run them directly, include them in dbt build, or execute them as part of a CI/CD workflow. This ensures that any issues with the data are caught early, maintaining trusted data across all data teams.
For a practical overview of data tests, source freshness checks, contracts, and unit testing, see dbt Tests in Action.
Best practices with dbt
Effective dbt projects keep transformation logic modular, documented, version-controlled, and tested so that it remains manageable as the number of models and downstream users grows.
- Modular code and reusable models: Always aim to write modular code. Break down complex SQL queries into smaller, reusable dbt models. This not only makes your codebase easier to manage but also promotes clarity and understanding among your data teams. For example, if several reports use the same revenue or customer-status calculation, that logic can live in one shared model rather than being recreated in multiple queries.
- Document everything: Don’t underestimate the power of documentation. With dbt, each model should be well-documented, and lineage graphs should be used to map data transformations. This practice not only helps in troubleshooting but also in onboarding new team members.Useful documentation should explain what a model represents, its level of detail, important fields, and how it is used downstream rather than simply restating what the SQL does.
- Follow software engineering principles: Embrace software engineering principles like version control, automated testing, and CI/CD pipelines. Version control makes transformation changes reviewable and reversible, while tests and CI help identify problems before changes reach production.
- Protect shared models: For production models used by multiple downstream consumers, dbt model contracts can enforce expected columns and data types, while model versioning helps teams introduce breaking changes without forcing every consumer to migrate at once. Contracts are particularly useful for shared or business-critical models rather than every model in a project. See our guides to dbt model contracts and dbt model versioning for more detail.
These best practices show how dbt is used in data engineering projects to keep transformation logic consistent, maintainable, and easier to change as projects grow.
Advanced dbt features
dbt packages
Reusability is a core principle of dbt. dbt packages allow you to share and reuse data transformation logic across different projects, improving efficiency and consistency.
Packages can also provide reusable tests, macros, code-generation utilities, or other capabilities that teams would otherwise need to build themselves.
Control structures and reusable logic
While dbt primarily uses SQL, it also provides ways to make transformation logic more flexible and reusable. Jinja templating allows teams to use variables, conditional logic, loops, and macros within SQL models, which helps avoid repeating the same patterns across multiple transformations. For use cases that are better suited to Python than SQL, dbt also supports Python models on compatible data platforms.
dbt in data engineering: Real-world applications
The following examples show what dbt is used for in data engineering projects, where teams need to standardize and maintain transformation logic across operational and analytical datasets.
dbt for ERP and CRM data transformation
An agricultural machinery company used dbt as the transformation and modeling layer in a Microsoft Fabric data platform. The project included 218 dbt models across three data layers, covering 110 source tables from two systems. dbt was used to clean, standardize, and aggregate ERP and CRM data into analytics-ready models, with testing, documentation, lineage, and Git-based version control supporting long-term maintenance.
The resulting platform replaced fragmented data and manual reconciliation with a standardized serving layer for reporting and analytics. Read the full data platform case study.
Where dbt fits into data engineering projects
More broadly, dbt can support different stages of data platform development and modernization. In our projects, this can include:
- Building transformation layers: organizing raw warehouse or lakehouse data into staging, intermediate, and business-ready models.
- Replacing fragmented transformation logic: moving logic from separate SQL scripts, stored procedures, or legacy ETL tools into modular dbt models.
- Improving data quality: adding tests, source freshness checks, documentation, and lineage alongside transformation logic.
- Supporting incremental pipelines: configuring models to process new or changed data without rebuilding entire datasets unnecessarily.
- Integrating transformation with delivery workflows: combining dbt with version control, CI/CD, and orchestration tools used across the wider data platform.
Teams can use dbt consulting and development services to assess an existing dbt project, migrate transformation logic, or build and integrate a new dbt transformation layer.
Data transformation with dbt
dbt is more than just another tool in the data engineer's toolkit. It represents a shift towards more collaborative, transparent, and efficient data practices. By embracing dbt, data practitioners can transform raw data into trusted analytics-ready datasets, adhering to software engineering best practices. Whether teams run dbt Core or Fusion locally, or work through the managed dbt platform, the same core approach applies: transformation logic can be versioned, tested, documented, and reviewed as code.
dbt is particularly relevant to data engineers, analytics engineers, and analysts who need to manage shared transformation logic across a modern data platform. By integrating dbt into your modern data stack, you can improve efficiency, accuracy, and collaboration, driving your organization toward data-driven success.
