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Data Build Tool (dbt)

Often, before you can analyze data and use it to feed ML algorithms, you need to clean and transform it. There are many approaches and tools to do this, and dbt has become one of the most popular options. In this post, we go through 5 great business benefits of dbt that explain why it is one of the best alternatives.

What is Data Build Tool?

dbt (Data Build Tool) does the T in ELT (Extract, Load, Transform) processes – it doesn’t extract or load data, but it’s extremely good at transforming data already loaded into your warehouse. dbt also enables analysts to work more like software engineers, offloading some work from them and creating the transformations that better suit their needs.


Benefits of using dbt

Data Quality

dbt makes it super easy to identify and fix issues. The data transformation process in dbt is divided into different phases (divide and conquer), and the lineage gives us a global view of their relationships. This lineage can be viewed entirely and filtered by interacting directly with it. In this way, you will be able to see the entire journey that our data make and obtain more information about those lineages that interest us.

dbt lineage graph

Also, tests are used to check if statements about models and other resources in your dbt project (e.g., sources, seeds, and snapshots) are correct. dbt will tell you if each test in your project passes or fails. To set up the proper tests, you must thoroughly study and understand the data.

You will be able to verify that your data is unique, not null, has the correct values, and if the relationship with other models is proper. This is very useful to detect errors in the data early and not continue working with incorrect data, making our ELT process more robust.

Along with the tests, data freshness helps us define the adequate time between the most recent record and a table to be considered “fresh.”

With all this, you might think that you must constantly log into dbt to verify that the lineage transformation process is correct and that your tests and updates are in good condition. Nothing is further from reality. dbt offers a notification hub with Slack to verify that everything is working correctly. This will inform us whenever a work process succeeds or fails and notify us of data updates. All this allows us to work with peace of mind and adequately control our operations.


Faster time to insight

Once implemented, dbt reduces the time you need to create the transformations required to get insightful information from raw data.

The use of resources will be minimal since the data warehouse supports all the computational work. This makes the transformation process faster, more secure, and easier to maintain. Also, it allows the use of development and deployment environments.

We can use packages to spend more time focusing on business logic and less time implementing code that someone else has already spent the time perfecting.


You can work with different databases with dbt-verified (Bigquery, Databricks, Postgres, and Redshift) and community-verified adapters (Athena, AWS Glue, MySQL, etc.). On this dbt page, you can see the extensive list of supported data platforms.

Maintenance costs

In addition to being an advantage in the data quality, the speed with which we can be notified and fix the problems makes maintenance costs go down. Modularity (different phases in which the transformation process is divided) helps us to solve problems as soon as possible. This modularized acts as a debugger with which we can detect the transformation where the issues happen.


We can reuse transformations thanks to the modularity above. The same transformation can be valid for different processes. dbt allows us to do this with the additional advantage of using package shortcuts, which makes it easier (even) to reuse the code.

In addition, transforming the data will be accessible because it only requires knowledge of SQL to do data transformations. With GitHub for version control, all this will make it accessible and understandable for people in the training process.

One point to note is how well the app is documented. You can quickly answer your questions on the website and in forums and discover features that will help you perfect your project.

All this will make adding new people to your project easy, which makes it highly scalable.


We hope now you know what is data build tool and why it’s great. Whether you’re a data expert or not, dbt is perfect for you. The learning curve is low, and its benefits are great. It’s easy to use, the results are surprisingly good, you can monitor the transformation process, and there’s version control. Plus, with lineage and notifications, you can spot failures and make your data transformation process more complete. Its versatility will make you run out of excuses since it is compatible with most databases.

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