Undoubtedly, one of the quickly developing technologies in the area of data and analytics engineering is the data build tool (dbt). As a tool for data transformation, data is essential to the T-stage of ELT pipelines. It enables teams to deploy analytics code successfully while following industry best practices for software engineering. These techniques include automated documentation generation, continuous integration, and deployment.
Although the firm that developed debt, Fisht’s own Analytics, offers a cloud deployment strategy for the tool, a sizable percentage of businesses still prefer to integrate data into their in-house data pipelines. Utilizing Apache Airflow, which enables users to author, schedule, and monitor processes, is the most popular way to accomplish this.
We will introduce a Python library in this article that enables easy dbt project integration with Apache Airflow. The intrinsic dependency graph created by dbt is preserved while this package allows the construction of unique Airflow Tasks for each dbt entity, such as models, tests, seeds, and snapshots.
Notably, one of the major benefits of this integration is the ability to individually re-trigger each activity. This translates to a high degree of flexibility and efficiency in managing the execution of dbt projects because you can quickly execute certain dbt tasks without rerunning the full workflow.
Dbt is offered in two flavors: dbt Cloud and dbt Core, as was already indicated. The former, which is a managed service provided by dbt Labs, has a user interface that enables customers to test and deploy analytics code. The open-source command-line program called dbt Core, on the other hand, is used to carry out the transformations that are specified in dbt projects. More Details here
