What the data build tool (dbt) is and how to use it

Modern data pipelines have a lot of moving parts. Extracting files from their source and loading them into a data warehouse is simple with the right tools. But transformations may be trickier, given the wide range of analytical and operational use cases for data.

In the past, a data engineer or database administrator would have to painstakingly transform data manually. Now, thanks to the data build tool (dbt), the entire process is much quicker and easier. With this open-source tool, analysts can transform raw data into clean tables using only structured query language (SQL) — no heavy engineering or endless back and forth required.

In this article, we break down what dbt software is, how it stacks up against the traditional “transformation” part of extract, transform, load (ETL) tools, and how you can start using it.

What is dbt and how does it work?

The dbt platform — developed and maintained by dbt Labs — is an open-source framework for analytics engineering. It lets teams write, test, and deploy SQL-based data transformations using software engineering best practices. 

Traditional ETL tools often feel like starting from scratch: You’re handed an empty workshop and expected to supply your own tools, build your own workbench, and craft everything manually. dbt flips that model — it’s more like walking into a modern, collaborative workshop already stocked with purpose-built tools, shared standards, and proven templates. You still do the craftsmanship, but with the infrastructure and support to move faster and more confidently.

Through automation, dbt removes the grunt work of manual transformation: It eliminates boilerplate SQL, enforces testing and documentation standards, and integrates seamlessly with modern ELT pipelines. The result is faster development cycles, more reliable data products, and greater confidence in your analytics.

What are dbt’s features?

Here are the features that differentiate the platform from traditional ETL tools and make dbt data more useful in a modern data stack: 

• Version control: The dbt platform lets your team track every change to your data models and allows multiple users to work on the same project simultaneously. This creates a clear history of your transformations, boosts collaboration, and means you always know who updated what and why. 
• Documentation and lineage: With dbt, documentation isn’t an afterthought. The platform generates easy-to-follow documents and visual lineage graphs showing how each model connects to others. Users can track and audit transformations and annotate their models to provide context.
• Testing and data quality checks: You can build tests directly into your models to catch issues like missing values, unexpected duplicates, or broken assumptions. This keeps data reliable before it reaches dashboards or reports.
• Modularity, reusability, and DRY logic: Using don’t repeat yourself (DRY) logic, the platform encourages small, reusable models, so you don’t have to use the same code across multiple queries. This keeps the project cleaner and makes updates far easier.
• Easy integration with data warehouses: You can easily integrate dbt with modern cloud warehouses like Snowflake, BigQuery, and Redshift. You just need to write the SQL to manipulate the data, and dbt handles the heavy lifting, running models as efficiently as possible in whatever data warehouse your team uses.

Why should you use dbt?

With dbt, you can reference and transform data without it ever leaving your warehouse. Here are a few reasons the platform has become mainstream.

Vastly improved runtimes

Long runtimes ruin your analysts’ productivity. It gets even worse when your visualization platform is stuck doing data transformation. Speed things up with dbt by pushing the number-crunching to the warehouse instead. The tool also uses incremental data models that refresh only new and changed records, meaning you skip full rebuilds and keep things streamlined.

Simplified data transformations

Why wait for an engineer to transform your data? In a dbt pipeline, analysts own transformations from start to finish. Instead of complex programming languages, they only need to learn simple SQL.

Easily maintained data documentation

The dbt platform cuts the “what am I even staring at?” feeling out of your workflow. Its documentation hub keeps all the information about your project and warehouse in one place, so even if an employee leaves, someone else can pick up where they left off. Clear model descriptions also add transparency, making your data much easier to trust and maintain.

How to use dbt

Using dbt is as simple as writing simple SQL to shape the data. The platform handles the rest through automation. But there are a few things you’ll need to check off before you get started, including:

• SQL skills
• A connected warehouse like Snowflake, BigQuery, or Redshift 
• A version control system like Git

Once you’re set up, dbt becomes the hub where your team builds, updates, and manages every data model in your pipeline — all without the slow, hands-on work traditional tools require.

What can dbt do for your data pipeline?

Here are the key ways dbt strengthens your data pipeline, adding real, practical value to how your team works with data:

• Letting analysts own transformations: Rather than requiring the expertise of a data engineer or database administrator, dbt gives analysts the tools they need to shape and clean data themselves.
• Improving data quality and trust: By building tests into models, dbt helps you catch issues before they reach your reports. Teams can rely on data instead of second-guessing which tables are accurate. 
• Creating consistent, standard workflows: The platform encourages the use of DRY logic, shared templates, and clear naming schemes. This helps keep your pipeline organized and prevents it from turning into a patchwork of one-off fixes.
• Speeding up iteration and maintenance: Because the tool uses version-controlled SQL, updates are quick and easy. Teams can make changes confidently without breaking other parts of the pipeline.

What is data modeling?

In analytics, data modeling means organizing scattered and mismatched data into usable representations of reality. You pull together tables from different systems and shape them into a single, easy-to-understand view of the business. It’s much easier to get value from your data once it’s organized, whether that’s through reports or dashboards, or even using it to make predictions. 

Well-designed models help different stakeholders make sense of data. They outline what the business collects, show how different datasets relate to one another, and explain the methods used to store and analyze the information.

What are dbt packages?

dbt packages are ready-made data toolkits. They’re self-contained projects with pre-written SQL designed to tackle specific problems, so you don’t have to start from scratch every time you need to transform data. Think of them like a library of useful modules you can plug into your own project to save time and cut down on hand-coding. 

How Fivetran complements dbt

Fivetran and dbt are a natural fit. Use Fivetran to get data into your warehouse, then use dbt to transform it once it’s there. By taking care of ingestion and the heavy ELT plumbing, Fivetran provides dbt with clean, consistent data to work with. 

Fivetran pulls data from all your sources and loads it into warehouses like Snowflake, BigQuery, or Redshift. It also manages schema drift and pipeline reliability, so you’re not constantly fixing broken connectors or updating mappings. This frees your analytics engineers from tedious maintenance, letting them focus on modeling, testing, and improving the actual data experience in dbt. The result is a smoother pipeline that breaks less and provides your team with ready-to-use data at every layer.

‍Get started for free or book a live demo to see how Fivetran can streamline your data pipeline.

FAQs

What’s the dbt tool used for?

The dbt tool is used for data transformation, allowing teams to build and test data models with version-controlled, modular SQL. It simplifies how analytics teams prepare reliable, analysis-ready data.

What’s a dbt database?

A dbt database refers to the tables and views dbt creates using its open-source framework to transform data. It turns raw data into clean, analytics-ready tables inside your existing warehouse.

What’s dbt programming?

In relation to the dbt platform, dbt programming means using the tool to apply code-like workflows to analytics. It brings software engineering principles to data analysis while modeling data using SQL.

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