What is dbt (data build tool)?
If you work with data engineering and data analysis, you may have already heard talk of dbt in the office landscape. But what exactly is dbt? And what are the benefits of using this framework?
Dbt stands for Data Build Tool, and is a framework that helps developers and analysts build, maintain and test data transformations. The framework has become extremely popular in recent years. This is largely as a result of increased demand for cloud-based data warehouses such as Snowflake, BigQuery and Databricks.
Dbt is built around SQL, but with extended functionality in the form of macros in the templating language Jinja. Dependencies between transformations are often very complex and vulnerable to the value chain stopping. The idea behind dbt is to make it easier to run transformations in the correct order.
In dbt, transformation is defined as individual models with dependencies to other models. When dbt runs, the code is compiled and dbt takes care of the job of running the SQL scripts in the correct sequence against your data platform. By transforming the data where it is located, instead of moving the data, dbt facilitates working with large amounts of data. Despite the fact that the tool is powerful and flexible enough to handle most transformation and modeling tasks, the learning curve is relatively low. This means that dbt quickly becomes a favorite among those with previous knowledge of SQL.
Efficient collaboration and easier testing
In addition to its core functionality, Dbt has distinguished itself by addressing a number of the most central issues that arise in a modern analysis environment. In the face of increasingly complex data flows, among other things, the possibility of effective collaboration is more important than ever. Being able to tell who did what, and when it was done, is crucial for success in larger teams. By being able to build robust CI/CD pipelines, you reduce the opportunities for human error in the rollout of solutions. Dbt integrates seamlessly with Git whether you work locally or in the dbt Cloud. You then have the same opportunities within collaboration, version control and CI/CD pipelines that software development has had available for a number of years using mature tools such as Github and Gitlab.
Another central issue is testing the quality of the data included in an analysis. Incorrect and missing data lead to incorrect and missing analyses. Identifying a good framework that addresses these issues can be challenging. In dbt, you can use pre-defined or self-written tests to validate the quality of the underlying data or the result of transformations. Dbt also has a built-in functionality to automatically generate a data catalog where you can see data lineage, tests and other relevant metadata for your dbt project.
Computas’ experienced data and smart analysis team has extensive experience in using dbt. A clear proof of particularly good DBT competence is that Computas is one of the few dbt partners in Norway.
– A product like dbt is something you will see in almost all modern data platforms today, says subject director for data and smart analysis at Computas Anders Elton.
– We have used dbt for several years now, and it is a mature tool that both traditional developers and analysts quickly feel familiar with – and like! Recently, we have registered a rapid increase in popularity and demand in the market, and it is also important for us to continue developing our own cutting-edge expertise. We do this, among other things, through a formal partnership with this technology supplier, concludes Elton.